| data science recommendation system: Recommender Systems Handbook Francesco Ricci, Lior Rokach, Bracha Shapira, 2015-11-17 This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems. |
| data science recommendation system: Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications Abhishek Majumder, Joy Lal Sarkar, Arindam Majumder, 2023-08-16 Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications captures the state of the art in usage of artificial intelligence in different types of recommendation systems and predictive analysis. The book provides guidelines and case studies for application of artificial intelligence in recommendation from expert researchers and practitioners. A detailed analysis of the relevant theoretical and practical aspects, current trends and future directions is presented. The book highlights many use cases for recommendation systems: · Basic application of machine learning and deep learning in recommendation process and the evaluation metrics · Machine learning techniques for text mining and spam email filtering considering the perspective of Industry 4.0 · Tensor factorization in different types of recommendation system · Ranking framework and topic modeling to recommend author specialization based on content. · Movie recommendation systems · Point of interest recommendations · Mobile tourism recommendation systems for visually disabled persons · Automation of fashion retail outlets · Human resource management (employee assessment and interview screening) This reference is essential reading for students, faculty members, researchers and industry professionals seeking insight into the working and design of recommendation systems. |
| data science recommendation system: Mahout in Action Sean Owen, B. Ellen Friedman, Robin Anil, Ted Dunning, 2011-10-04 Summary Mahout in Action is a hands-on introduction to machine learning with Apache Mahout. Following real-world examples, the book presents practical use cases and then illustrates how Mahout can be applied to solve them. Includes a free audio- and video-enhanced ebook. About the Technology A computer system that learns and adapts as it collects data can be really powerful. Mahout, Apache's open source machine learning project, captures the core algorithms of recommendation systems, classification, and clustering in ready-to-use, scalable libraries. With Mahout, you can immediately apply to your own projects the machine learning techniques that drive Amazon, Netflix, and others. About this Book This book covers machine learning using Apache Mahout. Based on experience with real-world applications, it introduces practical use cases and illustrates how Mahout can be applied to solve them. It places particular focus on issues of scalability and how to apply these techniques against large data sets using the Apache Hadoop framework. This book is written for developers familiar with Java -- no prior experience with Mahout is assumed. Owners of a Manning pBook purchased anywhere in the world can download a free eBook from manning.com at any time. They can do so multiple times and in any or all formats available (PDF, ePub or Kindle). To do so, customers must register their printed copy on Manning's site by creating a user account and then following instructions printed on the pBook registration insert at the front of the book. What's Inside Use group data to make individual recommendations Find logical clusters within your data Filter and refine with on-the-fly classification Free audio and video extras Table of Contents Meet Apache Mahout PART 1 RECOMMENDATIONS Introducing recommenders Representing recommender data Making recommendations Taking recommenders to production Distributing recommendation computations PART 2 CLUSTERING Introduction to clustering Representing data Clustering algorithms in Mahout Evaluating and improving clustering quality Taking clustering to production Real-world applications of clustering PART 3 CLASSIFICATION Introduction to classification Training a classifier Evaluating and tuning a classifier Deploying a classifier Case study: Shop It To Me |
| data science recommendation system: Recommender System with Machine Learning and Artificial Intelligence Sachi Nandan Mohanty, Jyotir Moy Chatterjee, Sarika Jain, Ahmed A. Elngar, Priya Gupta, 2020-07-08 This book is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. It comprehensively covers the topic of recommender systems, which provide personalized recommendations of items or services to the new users based on their past behavior. Recommender system methods have been adapted to diverse applications including social networking, movie recommendation, query log mining, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommendations in agricultural or healthcare domains and contexts, the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. This book illustrates how this technology can support the user in decision-making, planning and purchasing processes in agricultural & healthcare sectors. |
| data science recommendation system: Recommender Systems Charu C. Aggarwal, 2016-03-28 This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors. |
| data science recommendation system: Hands-On Recommendation Systems with Python Rounak Banik, 2018-07-31 With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web Key Features Build industry-standard recommender systems Only familiarity with Python is required No need to wade through complicated machine learning theory to use this book Book Description Recommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you'll get started with building and learning about recommenders as quickly as possible.. In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains. What you will learn Get to grips with the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content based engine to recommend movies based on movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative fltering Who this book is for If you are a Python developer and want to develop applications for social networking, news personalization or smart advertising, this is the book for you. Basic knowledge of machine learning techniques will be helpful, but not mandatory. |
| data science recommendation system: Statistical Methods for Recommender Systems Deepak K. Agarwal, Bee-Chung Chen, 2016-02-24 Designing algorithms to recommend items such as news articles and movies to users is a challenging task in numerous web applications. The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives. Major technical challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods), bilinear random-effects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such large-scale systems at Yahoo! and LinkedIn, and bridge the gap between theory and practice by illustrating complex concepts with examples from applications they are directly involved with. |
| data science recommendation system: Recommendation Engines Michael Schrage, 2020-09-01 How companies like Amazon, Netflix, and Spotify know what you might also like: the history, technology, business, and societal impact of online recommendation engines. Increasingly, our technologies are giving us better, faster, smarter, and more personal advice than our own families and best friends. Amazon already knows what kind of books and household goods you like and is more than eager to recommend more; YouTube and TikTok always have another video lined up to show you; Netflix has crunched the numbers of your viewing habits to suggest whole genres that you would enjoy. In this volume in the MIT Press's Essential Knowledge series, innovation expert Michael Schrage explains the origins, technologies, business applications, and increasing societal impact of recommendation engines, the systems that allow companies worldwide to know what products, services, and experiences you might also like. |
| data science recommendation system: Practical Recommender Systems Kim Falk, 2019-01-18 Summary Online recommender systems help users find movies, jobs, restaurants-even romance! There's an art in combining statistics, demographics, and query terms to achieve results that will delight them. Learn to build a recommender system the right way: it can make or break your application! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Recommender systems are everywhere, helping you find everything from movies to jobs, restaurants to hospitals, even romance. Using behavioral and demographic data, these systems make predictions about what users will be most interested in at a particular time, resulting in high-quality, ordered, personalized suggestions. Recommender systems are practically a necessity for keeping your site content current, useful, and interesting to your visitors. About the Book Practical Recommender Systems explains how recommender systems work and shows how to create and apply them for your site. After covering the basics, you'll see how to collect user data and produce personalized recommendations. You'll learn how to use the most popular recommendation algorithms and see examples of them in action on sites like Amazon and Netflix. Finally, the book covers scaling problems and other issues you'll encounter as your site grows. What's inside How to collect and understand user behavior Collaborative and content-based filtering Machine learning algorithms Real-world examples in Python About the Reader Readers need intermediate programming and database skills. About the Author Kim Falk is an experienced data scientist who works daily with machine learning and recommender systems. Table of Contents PART 1 - GETTING READY FOR RECOMMENDER SYSTEMS What is a recommender? User behavior and how to collect it Monitoring the system Ratings and how to calculate them Non-personalized recommendations The user (and content) who came in from the cold PART 2 - RECOMMENDER ALGORITHMS Finding similarities among users and among content Collaborative filtering in the neighborhood Evaluating and testing your recommender Content-based filtering Finding hidden genres with matrix factorization Taking the best of all algorithms: implementing hybrid recommenders Ranking and learning to rank Future of recommender systems |
| data science recommendation system: Building a Recommendation System with R Suresh K. Gorakala, Michele Usuelli, 2015-09-29 Learn the art of building robust and powerful recommendation engines using R About This Book Learn to exploit various data mining techniques Understand some of the most popular recommendation techniques This is a step-by-step guide full of real-world examples to help you build and optimize recommendation engines Who This Book Is For If you are a competent developer with some knowledge of machine learning and R, and want to further enhance your skills to build recommendation systems, then this book is for you. What You Will Learn Get to grips with the most important branches of recommendation Understand various data processing and data mining techniques Evaluate and optimize the recommendation algorithms Prepare and structure the data before building models Discover different recommender systems along with their implementation in R Explore various evaluation techniques used in recommender systems Get to know about recommenderlab, an R package, and understand how to optimize it to build efficient recommendation systems In Detail A recommendation system performs extensive data analysis in order to generate suggestions to its users about what might interest them. R has recently become one of the most popular programming languages for the data analysis. Its structure allows you to interactively explore the data and its modules contain the most cutting-edge techniques thanks to its wide international community. This distinctive feature of the R language makes it a preferred choice for developers who are looking to build recommendation systems. The book will help you understand how to build recommender systems using R. It starts off by explaining the basics of data mining and machine learning. Next, you will be familiarized with how to build and optimize recommender models using R. Following that, you will be given an overview of the most popular recommendation techniques. Finally, you will learn to implement all the concepts you have learned throughout the book to build a recommender system. Style and approach This is a step-by-step guide that will take you through a series of core tasks. Every task is explained in detail with the help of practical examples. |
| data science recommendation system: Machine Learning: Make Your Own Recommender System Oliver Theobald, 2018-10-06 Learn How to Make Your Own Recommender System in an Afternoon.Recommender systems are one of the most visible applications of machine learning and data mining today and their uncanny ability to convert our unspoken actions into items we desire is both addicting and concerning. And whether recommender systems excite or scare you, the best way to manage their influence and impact is to understand the architecture and algorithms that play on your personal data. Recommender systems are here to stay and for anyone beginning their journey in data science, this is a lucrative space for future employment.This book will get you up and running with the basics as well as the steps to coding your own recommender system. Exercises include predicting book recommendations, relevant house properties for online marketing purposes, and whether a user will click on an ad campaign. The contents of this book is designed for beginners with some background knowledge of data science, including classical statistics and computing programming. If this is your first exposure to data science, you may want to spend a few hours to read my first book Machine Learning for Absolute Beginners before you get started here.Topics covered in this book: Setting Up A Sandbox Environment With Jupyter NotebookWorking With DataData ReductionBuilding a Collaborative Filtering ModelBuilding a Content-Based Filtering ModelEvaluationPrivacy & EthicsFuture of Recommender SystemsPlease feel welcome to join this introductory course by buying a copy or sending a free sample to your preferred device. |
| data science recommendation system: Recommender Systems Dietmar Jannach, Markus Zanker, Alexander Felfernig, Gerhard Friedrich, 2010-09-30 In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems. |
| data science recommendation system: Recommendation Systems in Software Engineering Martin P. Robillard, Walid Maalej, Robert J. Walker, Thomas Zimmermann, 2014-04-30 With the growth of public and private data stores and the emergence of off-the-shelf data-mining technology, recommendation systems have emerged that specifically address the unique challenges of navigating and interpreting software engineering data. This book collects, structures and formalizes knowledge on recommendation systems in software engineering. It adopts a pragmatic approach with an explicit focus on system design, implementation, and evaluation. The book is divided into three parts: “Part I – Techniques” introduces basics for building recommenders in software engineering, including techniques for collecting and processing software engineering data, but also for presenting recommendations to users as part of their workflow. “Part II – Evaluation” summarizes methods and experimental designs for evaluating recommendations in software engineering. “Part III – Applications” describes needs, issues and solution concepts involved in entire recommendation systems for specific software engineering tasks, focusing on the engineering insights required to make effective recommendations. The book is complemented by the webpage rsse.org/book, which includes free supplemental materials for readers of this book and anyone interested in recommendation systems in software engineering, including lecture slides, data sets, source code, and an overview of people, groups, papers and tools with regard to recommendation systems in software engineering. The book is particularly well-suited for graduate students and researchers building new recommendation systems for software engineering applications or in other high-tech fields. It may also serve as the basis for graduate courses on recommendation systems, applied data mining or software engineering. Software engineering practitioners developing recommendation systems or similar applications with predictive functionality will also benefit from the broad spectrum of topics covered. |
| data science recommendation system: Building Recommender Systems with Machine Learning and AI. Frank Kane, 2018 Automated recommendations are everywhere: Netflix, Amazon, YouTube, and more. Recommender systems learn about your unique interests and show the products or content they think you'll like best. Discover how to build your own recommender systems from one of the pioneers in the field. Frank Kane spent over nine years at Amazon, where he led the development of many of the company's personalized product recommendation technologies. In this course, he covers recommendation algorithms based on neighborhood-based collaborative filtering and more modern techniques, including matrix factorization and even deep learning with artificial neural networks. Along the way, you can learn from Frank's extensive industry experience and understand the real-world challenges of applying these algorithms at a large scale with real-world data. You can also go hands-on, developing your own framework to test algorithms and building your own neural networks using technologies like Amazon DSSTNE, AWS SageMaker, and TensorFlow. |
| data science recommendation system: Programming Collective Intelligence Toby Segaran, 2007-08-16 Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details. -- Dan Russell, Google Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths. -- Tim Wolters, CTO, Collective Intellect |
| data science recommendation system: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| data science recommendation system: The Adaptive Web Peter Brusilovski, Alfred Kobsa, Wolfgang Nejdl, 2007-04-24 This state-of-the-art survey provides a systematic overview of the ideas and techniques of the adaptive Web and serves as a central source of information for researchers, practitioners, and students. The volume constitutes a comprehensive and carefully planned collection of chapters that map out the most important areas of the adaptive Web, each solicited from the experts and leaders in the field. |
| data science recommendation system: Machine Learning Design Patterns Valliappa Lakshmanan, Sara Robinson, Michael Munn, 2020-10-15 The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly |
| data science recommendation system: Engineering MLOps Emmanuel Raj, 2021-04-19 Get up and running with machine learning life cycle management and implement MLOps in your organization Key FeaturesBecome well-versed with MLOps techniques to monitor the quality of machine learning models in productionExplore a monitoring framework for ML models in production and learn about end-to-end traceability for deployed modelsPerform CI/CD to automate new implementations in ML pipelinesBook Description Engineering MLps presents comprehensive insights into MLOps coupled with real-world examples in Azure to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production. The book begins by familiarizing you with the MLOps workflow so you can start writing programs to train ML models. Then you'll then move on to explore options for serializing and packaging ML models post-training to deploy them to facilitate machine learning inference, model interoperability, and end-to-end model traceability. You'll learn how to build ML pipelines, continuous integration and continuous delivery (CI/CD) pipelines, and monitor pipelines to systematically build, deploy, monitor, and govern ML solutions for businesses and industries. Finally, you'll apply the knowledge you've gained to build real-world projects. By the end of this ML book, you'll have a 360-degree view of MLOps and be ready to implement MLOps in your organization. What you will learnFormulate data governance strategies and pipelines for ML training and deploymentGet to grips with implementing ML pipelines, CI/CD pipelines, and ML monitoring pipelinesDesign a robust and scalable microservice and API for test and production environmentsCurate your custom CD processes for related use cases and organizationsMonitor ML models, including monitoring data drift, model drift, and application performanceBuild and maintain automated ML systemsWho this book is for This MLOps book is for data scientists, software engineers, DevOps engineers, machine learning engineers, and business and technology leaders who want to build, deploy, and maintain ML systems in production using MLOps principles and techniques. Basic knowledge of machine learning is necessary to get started with this book. |
| data science recommendation system: Artificial Intelligence with Python Prateek Joshi, 2017-01-27 Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application. |
| data science recommendation system: Mining of Massive Datasets Jure Leskovec, Jurij Leskovec, Anand Rajaraman, Jeffrey David Ullman, 2014-11-13 Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets. |
| data science recommendation system: A History of Silicon Valley Piero Scaruffi, 2015-12-11 This book is the first history of Silicon Valley from 1900 to the 2010s. It is a comprehensive study of the greatest creation of wealth in the history of the world, from the establishment of Stanford University to the age of social media. The underlying objective is to find the reason why it was Silicon Valley, and not some place on the East Coast or in Europe, that became the creative technological hub of the 21st century. Silicon Valley did not happen in a vacuum: the book also explores the surrounding social and cultural environment of the Bay Area. This green book follows the red book od 2012, which was the (sold out) first edition coauthored with Arun Rao, and the blue book, which was Arun's proof-edited and expanded second edition of all chapters. The 600-page blue book is still available and contains both my old chapters and Arun's chapters. This 500-page green edition contains only my chapters (basically, the chronology) updated to 2015 and with many additions to early chapters and a new chapter on Asia. |
| data science recommendation system: Modern Artificial Intelligence and Data Science 2024 Abdellah Idrissi, |
| data science recommendation system: Data Mining Approaches for Big Data and Sentiment Analysis in Social Media Brij Gupta, Ahmed A. Abd El-Latif, Dragan Perakovic, 2021 This book explores the key concepts of data mining and utilizing them on online social media platforms, offering valuable insight into data mining approaches for big data and sentiment analysis in online social media and covering many important security and other aspects and current trends-- |
| data science recommendation system: Data, Engineering and Applications Rajesh Kumar Shukla, Jitendra Agrawal, Sanjeev Sharma, Geetam Singh Tomer, 2019-03-18 This book presents a compilation of current trends, technologies, and challenges in connection with Big Data. Many fields of science and engineering are data-driven, or generate huge amounts of data that are ripe for the picking. There are now more sources of data than ever before, and more means of capturing data. At the same time, the sheer volume and complexity of the data have sparked new developments, where many Big Data problems require new solutions. Given its scope, the book offers a valuable reference guide for all graduate students, researchers, and scientists interested in exploring the potential of Big Data applications. |
| data science recommendation system: Soft Computing for Problem Solving Kedar Nath Das, Jagdish Chand Bansal, Kusum Deep, Atulya K. Nagar, Ponnambalam Pathipooranam, Rani Chinnappa Naidu, 2019-11-27 This two-volume book presents the outcomes of the 8th International Conference on Soft Computing for Problem Solving, SocProS 2018. This conference was a joint technical collaboration between the Soft Computing Research Society, Liverpool Hope University (UK), and Vellore Institute of Technology (India), and brought together researchers, engineers and practitioners to discuss thought-provoking developments and challenges in order to select potential future directions. The book highlights the latest advances and innovations in the interdisciplinary areas of soft computing, including original research papers on algorithms (artificial immune systems, artificial neural networks, genetic algorithms, genetic programming, and particle swarm optimization) and applications (control systems, data mining and clustering, finance, weather forecasting, game theory, business and forecasting applications). It offers a valuable resource for both young and experienced researchers dealing with complex and intricate real-world problems that are difficult to solve using traditional methods. |
| data science recommendation system: Trends of Data Science and Applications Siddharth Swarup Rautaray, Phani Pemmaraju, Hrushikesha Mohanty, 2021-03-21 This book includes an extended version of selected papers presented at the 11th Industry Symposium 2021 held during January 7–10, 2021. The book covers contributions ranging from theoretical and foundation research, platforms, methods, applications, and tools in all areas. It provides theory and practices in the area of data science, which add a social, geographical, and temporal dimension to data science research. It also includes application-oriented papers that prepare and use data in discovery research. This book contains chapters from academia as well as practitioners on big data technologies, artificial intelligence, machine learning, deep learning, data representation and visualization, business analytics, healthcare analytics, bioinformatics, etc. This book is helpful for the students, practitioners, researchers as well as industry professional. |
| data science recommendation system: Recommender Systems Handbook Francesco Ricci, Lior Rokach, Bracha Shapira, 2022-04-21 This third edition handbook describes in detail the classical methods as well as extensions and novel approaches that were more recently introduced within this field. It consists of five parts: general recommendation techniques, special recommendation techniques, value and impact of recommender systems, human computer interaction, and applications. The first part presents the most popular and fundamental techniques currently used for building recommender systems, such as collaborative filtering, semantic-based methods, recommender systems based on implicit feedback, neural networks and context-aware methods. The second part of this handbook introduces more advanced recommendation techniques, such as session-based recommender systems, adversarial machine learning for recommender systems, group recommendation techniques, reciprocal recommenders systems, natural language techniques for recommender systems and cross-domain approaches to recommender systems. The third part covers a wide perspective to the evaluation of recommender systems with papers on methods for evaluating recommender systems, their value and impact, the multi-stakeholder perspective of recommender systems, the analysis of the fairness, novelty and diversity in recommender systems. The fourth part contains a few chapters on the human computer dimension of recommender systems, with research on the role of explanation, the user personality and how to effectively support individual and group decision with recommender systems. The last part focusses on application in several important areas, such as, food, music, fashion and multimedia recommendation. This informative third edition handbook provides a comprehensive, yet concise and convenient reference source to recommender systems for researchers and advanced-level students focused on computer science and data science. Professionals working in data analytics that are using recommendation and personalization techniques will also find this handbook a useful tool. |
| data science recommendation system: Collaborative Filtering Recommender Systems Michael D. Ekstrand, John T. Riedl, Joseph A. Konstan, 2011 Collaborative Filtering Recommender Systems discusses a wide variety of the recommender choices available and their implications, providing both practitioners and researchers with an introduction to the important issues underlying recommenders and current best practices for addressing these issues. |
| data science recommendation system: Cochrane Handbook for Systematic Reviews of Interventions Julian P. T. Higgins, Sally Green, 2008-11-24 Healthcare providers, consumers, researchers and policy makers are inundated with unmanageable amounts of information, including evidence from healthcare research. It has become impossible for all to have the time and resources to find, appraise and interpret this evidence and incorporate it into healthcare decisions. Cochrane Reviews respond to this challenge by identifying, appraising and synthesizing research-based evidence and presenting it in a standardized format, published in The Cochrane Library (www.thecochranelibrary.com). The Cochrane Handbook for Systematic Reviews of Interventions contains methodological guidance for the preparation and maintenance of Cochrane intervention reviews. Written in a clear and accessible format, it is the essential manual for all those preparing, maintaining and reading Cochrane reviews. Many of the principles and methods described here are appropriate for systematic reviews applied to other types of research and to systematic reviews of interventions undertaken by others. It is hoped therefore that this book will be invaluable to all those who want to understand the role of systematic reviews, critically appraise published reviews or perform reviews themselves. |
| data science recommendation system: Data Science and Innovations for Intelligent Systems Kavita Taneja, Harmunish Taneja, Kuldeep Kumar, Arvind Selwal, Eng Lieh Ouh, 2021-09-30 Data science is an emerging field and innovations in it need to be explored for the success of society 5.0. This book not only focuses on the practical applications of data science to achieve computational excellence, but also digs deep into the issues and implications of intelligent systems. This book highlights innovations in data science to achieve computational excellence that can optimize performance of smart applications. The book focuses on methodologies, framework, design issues, tools, architectures, and technologies necessary to develop and understand data science and its emerging applications in the present era. Data Science and Innovations for Intelligent Systems: Computational Excellence and Society 5.0 is useful for the research community, start-up entrepreneurs, academicians, data-centered industries, and professeurs who are interested in exploring innovations in varied applications and the areas of data science. |
| data science recommendation system: Atomic Habits James Clear, 2018-10-16 The #1 New York Times bestseller. Over 20 million copies sold! Translated into 60+ languages! Tiny Changes, Remarkable Results No matter your goals, Atomic Habits offers a proven framework for improving--every day. James Clear, one of the world's leading experts on habit formation, reveals practical strategies that will teach you exactly how to form good habits, break bad ones, and master the tiny behaviors that lead to remarkable results. If you're having trouble changing your habits, the problem isn't you. The problem is your system. Bad habits repeat themselves again and again not because you don't want to change, but because you have the wrong system for change. You do not rise to the level of your goals. You fall to the level of your systems. Here, you'll get a proven system that can take you to new heights. Clear is known for his ability to distill complex topics into simple behaviors that can be easily applied to daily life and work. Here, he draws on the most proven ideas from biology, psychology, and neuroscience to create an easy-to-understand guide for making good habits inevitable and bad habits impossible. Along the way, readers will be inspired and entertained with true stories from Olympic gold medalists, award-winning artists, business leaders, life-saving physicians, and star comedians who have used the science of small habits to master their craft and vault to the top of their field. Learn how to: make time for new habits (even when life gets crazy); overcome a lack of motivation and willpower; design your environment to make success easier; get back on track when you fall off course; ...and much more. Atomic Habits will reshape the way you think about progress and success, and give you the tools and strategies you need to transform your habits--whether you are a team looking to win a championship, an organization hoping to redefine an industry, or simply an individual who wishes to quit smoking, lose weight, reduce stress, or achieve any other goal. |
| data science recommendation system: Proceedings of International Conference on Data Science and Applications Mukesh Saraswat, Chandreyee Chowdhury, Chintan Kumar Mandal, Amir H. Gandomi, 2023-02-16 This book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2022), organized by Soft Computing Research Society (SCRS) and Jadavpur University, Kolkata, India, from 26 to 27 March 2022. It covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing. |
| data science recommendation system: Data Science in the Medical Field Seifedine Kadry, Shubham Mahajan, 2024-09-30 ata science has the potential to influence and improve fundamental services such as the healthcare sector. This book recognizes this fact by analyzing the potential uses of data science in healthcare. Every human body produces 2 TB of data each day. This information covers brain activity, stress level, heart rate, blood sugar level, and many other things. More sophisticated technology, such as data science, allows clinicians and researchers to handle such a massive volume of data to track the health of patients. The book focuses on the potential and the tools of data science to identify the signs of illness at an extremely early stage. • Shows how improving automated analytical techniques can be used to generate new information from data for healthcare applications• Combines a number of related fields, with a particular emphasis on machine learning, big data analytics, statistics, pattern recognition, computer vision, and semantic web technologies• Provides information on the cutting-edge data science tools required to accelerate innovation for healthcare organizations and patients by reading this book |
| data science recommendation system: Recommender Systems Monideepa Roy, Pushpendu Kar, Sujoy Datta, 2023-06-19 Recommender Systems: A Multi-Disciplinary Approach presents a multi-disciplinary approach for the development of recommender systems. It explains different types of pertinent algorithms with their comparative analysis and their role for different applications. This book explains the big data behind recommender systems, the marketing benefits, how to make good decision support systems, the role of machine learning and artificial networks, and the statistical models with two case studies. It shows how to design attack resistant and trust-centric recommender systems for applications dealing with sensitive data. Features of this book: Identifies and describes recommender systems for practical uses Describes how to design, train, and evaluate a recommendation algorithm Explains migration from a recommendation model to a live system with users Describes utilization of the data collected from a recommender system to understand the user preferences Addresses the security aspects and ways to deal with possible attacks to build a robust system This book is aimed at researchers and graduate students in computer science, electronics and communication engineering, mathematical science, and data science. |
| data science recommendation system: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians. |
| data science recommendation system: Computational Intelligence in Data Science Lekshmi Kalinathan, Priyadharsini R., Madheswari Kanmani, Manisha S., 2022-09-28 This book constitutes the refereed post-conference proceedings of the Fifth IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2022, held virtually, in March 2022. The 28 revised full papers presented were carefully reviewed and selected from 96 submissions. The papers cover topics such as computational intelligence for text analysis; computational intelligence for image and video analysis; blockchain and data science. |
| data science recommendation system: Responsible Data Science Jimson Mathew, G. Santhosh Kumar, Deepak P., Joemon M. Jose, 2022-11-14 This book comprises select proceedings of the 7th International Conference on Data Science and Engineering (ICDSE 2021). The contents of this book focus on responsible data science. This book tries to integrate research across diverse topics related to data science, such as fairness, trust, ethics, confidentiality, transparency, and accuracy. The chapters in this book represent research from different perspectives that offer novel theoretical implications that span multiple disciplines. The book will serve as a reference resource for researchers and practitioners in academia and industry. |
| data science recommendation system: Advanced Analytics with Spark Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills, 2015-04-02 In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—classification, collaborative filtering, and anomaly detection among others—to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find these patterns useful for working on your own data applications. Patterns include: Recommending music and the Audioscrobbler data set Predicting forest cover with decision trees Anomaly detection in network traffic with K-means clustering Understanding Wikipedia with Latent Semantic Analysis Analyzing co-occurrence networks with GraphX Geospatial and temporal data analysis on the New York City Taxi Trips data Estimating financial risk through Monte Carlo simulation Analyzing genomics data and the BDG project Analyzing neuroimaging data with PySpark and Thunder |
| data science recommendation system: The Decision Maker's Handbook to Data Science Stylianos Kampakis, 2019-11-26 Data science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a confident understanding of data science and its application in their organization. It is easy for novices to the subject to feel paralyzed by intimidating buzzwords, but what many don’t realize is that data science is in fact quite multidisciplinary—useful in the hands of business analysts, communications strategists, designers, and more. With the second edition of The Decision Maker’s Handbook to Data Science, you will learn how to think like a veteran data scientist and approach solutions to business problems in an entirely new way. Author Stylianos Kampakis provides you with the expertise and tools required to develop a solid data strategy that is continuously effective. Ethics and legal issues surrounding data collection and algorithmic bias are some common pitfalls that Kampakis helps you avoid, while guiding you on the path to build a thriving data science culture at your organization. This updated and revised second edition, includes plenty of case studies, tools for project assessment, and expanded content for hiring and managing data scientists Data science is a language that everyone at a modern company should understand across departments. Friction in communication arises most often when management does not connect with what a data scientist is doing or how impactful data collection and storage can be for their organization. The Decision Maker’s Handbook to Data Science bridges this gap and readies you for both the present and future of your workplace in this engaging, comprehensive guide. What You Will Learn Understand how data science can be used within your business. Recognize the differences between AI, machine learning, and statistics.Become skilled at thinking like a data scientist, without being one.Discover how to hire and manage data scientists.Comprehend how to build the right environment in order to make your organization data-driven. Who This Book Is For Startup founders, product managers, higher level managers, and any other non-technical decision makers who are thinking to implement data science in their organization and hire data scientists. A secondary audience includes people looking for a soft introduction into the subject of data science. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
RESUME ANALYSIS AND SKILLS, JOB RECOMMEND…
This study creates and puts into practice a recommendation system for online job searching to lessen this tedious task. In this study, an armature has been proposed to prize the most ... Importing essential …
MediSense: A Smart Disease Prediction and Drug Recom…
Recommendation System Leveraging Machine and Deep Learning Techniques Pantulu Puneeth Raj Gurunanak Institute of Technology Dept. AI and Data Science Ibrahimpatnam, R.R, Telangana, India …
MUSIC RECOMMENDTAION SYSTEM USING BIGDATA
The "Music Recommendation System Using Big Data" project represents a groundbreaking exploration into the realms of music discovery and personalization. In the era of vast digital music libraries …
FOOD RECOMMENDATION SYSTEM: A SYSTEMATIC R…
The system learns from its training data, which includes daily calorie intake and food consumption patterns, to predict the recommended meal plan for a user. We evaluated the system on a dataset, and …
Recipe Recommendation System using Machine Lear…
range of food data or any meal data that is, likely combinations of recipes. In this project, we will construct a food recommendation system using big data analytics. 1.1 Aim And Objectives The …
Two Decades of Recommender Systems at A…
A Present-Day Perspective on Recommendation and Collaborative Filtering A s a PhD student who uses collaborative filtering in my work to introduce customized recommendation …
Machine Learning-Based Crop Recommendation System …
Conclusion:- The reviewed studies collectively emphasize the promise of machine learning and data science in revolutionizing ... Crop Recommendation System Machine learning algorithms …
Recommendation System for Netflix - Vrije Universiteit Am…
Faculty of Science Business Analytics Master of Science Business Analytics Recommendation System for Netflix by Leidy Esperanza MOLINA FERNÁNDEZ Providing a useful suggestion of …
Investment Recommendation System using Machine learni…
3) “Implementation of Fund Recommendation System Using Machine Learning” Chae-eun Park and Dong- seok Lee have devised a Fund Recommendation System utilizing Machine Learning …
Restaurant Recommendation System using Machine Lear…
The available recommendation system utilizes techniques across various fields, such as machine learning, data mining, database, statistics, similarity testing, etc. It generates predictions of user …
Intelligent book recommendation system u…
5Assistant Professor, Department of Computer Science and Engineering, Vignan’s Foundation for Science,T echnology and Research,V adlamudi, GunturA, ndhra Pradesh, India. 1Email: …
Crop Recommendation System Using Deep Learnin…
time data integration to deliver a sophisticated crop recommendation system. In an era where data-driven decision-making is pivotal, Crop Recommendation System emerges as a …
Recipe Recommendation System Using Machine Lear…
They develop a recipe recommendation system that utilizes content-based filtering and collaborative filtering techniques to provide personalized food suggestions. The paper reviews various approaches, …
Milvus: A Purpose-Built Vector Data Management …
vector data in data science and AI applications. This trend is fueled by the proliferation of unstructured data and machine learning (ML), where ML models usually transform unstructured data into …
A Hybrid Recommendation System for Marine Science …
Sensors 2020, 20, 6414 2 of 17 data [10]. It is an e ective method to improve retrieval capabilities to help users obtain the required data, i.e., news recommendations, music recommendations, and ...
Recommendation System on Travel Destination based on …
A brief history of the recommendation system describes various forms of recommendation systems including the current state-of-the-art techniques for the travel recommendation system. Past …
DIET RECOMMENDATION SYSTEM USING MACHINE …
give students examples from real-world situations to help them fully comprehend recommendation systems. Gao et al. (2017) [3] introduced a computational framework for a personalized diet …
A Deep Learning Algorithm for Music Information Retrieval ...
Recommendation System Hongxia Liu1 and Chenjing Zhao2 1School of music and dance, ... data for model training and utilizes the trained model for classification or prediction. In the Internet ... With the …
Natural Language Processing via LDA Topic …
building smart recommendation system in online communities. KEYWORDS Recommendation systems; Natural Language Processing; Topic modeling; Semantic analysis; Online community 1. …
CS224W Project Report Product Recommendation …
This system returns a random rating for each (product, customer) pair. Because the way it works, we expect it to have the worst performance. We will use this baseline system for comparisons with …
AI-Powered E-commerce Recommendation System
recommendation system tailored for an online grocery platform. By leveraging advanced algorithms, the system delivers personalized suggestions that adapt to customer preferences, ensuring a …
TOWARDS PERSONALIZED HEALTHCARE - AN INTELLI…
A critical aspect of intelligent medication recommendation systems is the selection and utilization of diverse data sources. Previous research has explored the integration of electronic health ...
An Effective Recommendation System to Forecast the Bes…
valid student data. The dataset consists of those students' records who are passed out all the learning year beginning from class 10th to the class …
Insurance Recommendation System - IJISRT
Volume 11, Issue 3, March 2023 International Journal of Innovative Science and Research Technology ISSN No:-2456-2165 IJISRT23MAR1661 www.ijisrt.com 1922 Insurance Recommendation …
An E-Commerce Based Personalized Health Product …
transferred data. The E-commerce recommendation system can quickly extract information and predict accurately. The E-commerce recommendation system also worked with real-time data. The …
CLIMASOUND: A WEATHER-BASED SONG RECOMMEN…
*5Head Of Artificial Intelligence And Data Science Department Of Computer Engineering Dr. D.Y. Patil College Of Engineering And Innovation, Varale, Pune, India. ABSTRACT The main objective of …
Evaluation and optimization of intelligent recommendation …
Proceedings of the 6th International Conference on Computing and Data Science. ... Generally speaking, the recommendation system has much data information, far greater than the user's …
A Deep Learning Approach to Job Recommendation Anal…
the author recommendation systems development. In contrast to natural present a system for recommending relevant job listings to students using machine learning and natural language processing …
Personalized Recommendation System
The recommendation model uses neural networks, including autoencoders and collaborative filtering, to understand complex links between users and items. The project also looks at adding …
Food Recommender System: A Review on Techniques, Da…
feedback and data readily available within the system to improve the performance and accuracy of the recommendation system. The system can better understand the user's preferences and behavior by …
Movie Recommendation System using Naive Bayes A…
In this project, we propose a Movie Recommendation System by combining the Naive Bayes Algorithm with Collaborative filtering. Keywords: Sentiment Analysis; Collaborative …
ENHANCING PET ADOPTION AND WELFARE USING A RE…
The proposed system addresses these challenges by implementing an advanced recommendation system designed to enhance pet adoption and welfare. The system leverages sophisticated …
Food recommendation system for the elderly - Res…
4.1 Data Conversion: The process of converting data to fit in the data analysis, such as the conversion of a text-format data to be in a structured form. 4.2 Data Reduction: the use of various ...
cs246.stanford
Recommender system suggests Megadeth from data collected about customer X 1/25/22 Jure Leskovec, Stanford CS246: Mining Massive Datasets 3. Items Retrieval Recommendations Products, web sites, …
Fertilizer Recommendation and Crop prediction using M…
optimal yield. In this research paper, we propose a machine learning-based approach for crop and fertilizer recommendation. Our approach uses a dataset of soil and weather data along …
Predicting Consumer Behavior in E-Commerce Using Reco…
machine learning algorithms, and big data analytics, recommendation systems can process vast amounts of information to generate accurate and timely predictions of consumer preferences and future …
MOVIE RECOMMENDATION SYSTEM APPROACH USIN…
research paper deals with movie recommendations and logic behind movie recommendation system, traditional movie recommendation systems, issues related to traditional movie recommendation …
MOVIE RECOMMENDATION SYSTEM USING OPTIMIZE…
In conclusion, an RNN-based movie recommendation system can be useful for giving users tailored movie suggestions. The caliber and volume of the data used to train the RNN model, however, are what …
A hybrid statistical-fuzzy recommendation system fo…
Mar 8, 2025 · A recommendation system (RS) utilizes advanced algorithms to deliver personalized suggestions based on user preferences, history, and behavior, thereby enhancing the user experience by …
Personalized medical recommendation system s…
Personalized medical recommendation system supported by medical data Winston Wang Phillips Exeter Academy, 20 Main Street, Exeter, NH 03833, USA wwang7@exeter.edu Abstract. A …
MOVIE RECOMMENDATION SYSTEM USING NLP - IRJM…
MOVIE RECOMMENDATION SYSTEM USING NLP Mr. Darshan Jain*1, Mr. Amaan Malik*2, Mr. Ayush Pathak*3 *1,2Students, Computer Science and Engineering, Shri Shankaracharya …
Crop Recommendation System using Machine Lear…
This proposed system developed a crop recommendation system for smart farming. In this research paper reviewed various machine learning algorithms like CHAID, KNN, K-means, Decision Tree, Neural …
PERSONALIZED SKINCARE PRODUCTS RECOMMEND…
2. The Proposed System The personalized skincare product recommendation system consists of several key components: User Profiling: Collects data on user skin type, concerns, and preferences through …
From Data to Harvest Recommendation System
Recommendation System Isaac Malsawma1, R. Chawngsangpuii2*and Lalhmingliana3 1Department of Information Technology, Mizoram University,Aizawl, India ... A key component of data …
Book Recommendation System - IRJET
Book Recommendation System Ms. Janhavi Surendra Khot1, Mr. D.A. Patil2 1Student, Dept. of Information Technology, Sant Rawool Maharaj Mahavidyalaya, Kudal, ... (Data Science) etc. My system …
Medicine Recommendation System - IRJET
The basic aim of Medicine Recommendation System is to design an effective and accurate system for predicting proper medication for patients. As large amount of historical data is …
Design and Implementation of Book Recommendation …
system for information recommendation plays a significant role in improving the learning efficiency of borrowers and book management [1]. The research on book recommendation system has been a …
Personalized Recommendation Systems …
the proposed approach in real -world recommendation scenarios. collaborative data, showcasing the power of deep learning The main contributions of this research encompass: in …
Food Recommendation System - JETIR
Food Recommendation System 1Ainsley Cardozo, 2Mahesh Poojari, 3Jobin Joseph, 4Omprakash Yadav 1 Student,2 3Student, 4Supervisor 1Computer Engineering, ... Pandas: Pandas is an open-source …
Ingredient Detection and Recipe Recommendation U…
database, the system suggests top-N recipes that closely match the user's input. The FastAPI backend orchestrates these processes seamlessly, receiving image data from the front end, performing …
