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| cold start problem in recommender systems: 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. |
| cold start problem in recommender systems: Fashion Recommender Systems Nima Dokoohaki, 2020-11-04 This book includes the proceedings of the first workshop on Recommender Systems in Fashion 2019. It presents a state of the art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail and fashion. The volume covers contributions from academic as well as industrial researchers active within this emerging new field. Recommender Systems are often used to solve different complex problems in this scenario, such as social fashion-based recommendations (outfits inspired by influencers), product recommendations, or size and fit recommendations. The impact of social networks and the influence that fashion influencers have on the choices people make for shopping is undeniable. For instance, many people use Instagram to learn about fashion trends from top influencers, which helps them to buy similar or even exact outfits from the tagged brands in the post. When traced, customers’ social behavior can be a very useful guide for online shopping websites, providing insights on the styles the customers are really interested in, and hence aiding the online shops in offering better recommendations and facilitating customers quest for outfits. Another well known difficulty with recommendation of similar items is the large quantities of clothing items which can be considered similar, but belong to different brands. Relying only on implicit customer behavioral data will not be sufficient in the coming future to distinguish between for recommendation that will lead to an item being purchased and kept, vs. a recommendation that might result in either the customer not following it, or eventually return the item. Finding the right size and fit for clothes is one of the major factors not only impacting customers purchase decision, but also their satisfaction from e-commerce fashion platforms. Moreover, fashion articles have important sizing variations. Finally, customer preferences towards perceived article size and fit for their body remain highly personal and subjective which influences the definition of the right size for each customer. The combination of the above factors leaves the customers alone to face a highly challenging problem of determining the right size and fit during their purchase journey, which in turn has resulted in having more than one third of apparel returns to be caused by not ordering the right article size. This challenge presents a huge opportunity for research in intelligent size and fit recommendation systems and machine learning solutions with direct impact on both customer satisfaction and business profitability. |
| cold start problem in recommender systems: 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. |
| cold start problem in recommender systems: Intelligent Data Communication Technologies and Internet of Things Jude Hemanth, Robert Bestak, Joy Iong-Zong Chen, 2021-02-12 This book solicits the innovative research ideas and solutions for almost all the intelligent data intensive theories and application domains. The proliferation of various mobile and wireless communication networks has paved way to foster a high demand for intelligent data processing and communication technologies. The potential of data in wireless mobile networks is enormous, and it constitutes to improve the communication capabilities profoundly. As the networking and communication applications are becoming more intensive, the management of data resources and its flow between various storage and computing resources are posing significant research challenges to both ICT and data science community. The general scope of this book covers the design, architecture, modeling, software, infrastructure and applications of intelligent communication architectures and systems for big data or data-intensive applications. In particular, this book reports the novel and recent research works on big data, mobile and wireless networks, artificial intelligence, machine learning, social network mining, intelligent computing technologies, image analysis, robotics and autonomous systems, data security and privacy. |
| cold start problem in recommender systems: 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. |
| cold start problem in recommender systems: Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms Management Association, Information Resources, 2020-12-05 Genetic programming is a new and evolutionary method that has become a novel area of research within artificial intelligence known for automatically generating high-quality solutions to optimization and search problems. This automatic aspect of the algorithms and the mimicking of natural selection and genetics makes genetic programming an intelligent component of problem solving that is highly regarded for its efficiency and vast capabilities. With the ability to be modified and adapted, easily distributed, and effective in large-scale/wide variety of problems, genetic algorithms and programming can be utilized in many diverse industries. This multi-industry uses vary from finance and economics to business and management all the way to healthcare and the sciences. The use of genetic programming and algorithms goes beyond human capabilities, enhancing the business and processes of various essential industries and improving functionality along the way. The Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms covers the implementation, tools and technologies, and impact on society that genetic programming and algorithms have had throughout multiple industries. By taking a multi-industry approach, this book covers the fundamentals of genetic programming through its technological benefits and challenges along with the latest advancements and future outlooks for computer science. This book is ideal for academicians, biological engineers, computer programmers, scientists, researchers, and upper-level students seeking the latest research on genetic programming. |
| cold start problem in recommender systems: 2020 Seventh International Conference on Software Defined Systems (SDS) IEEE Staff, 2020-04-20 Software Defined Systems (SDS) are systems that have added software components which help abstract actual IT equipment and other layers One classical example, of course, are hypervisors Such separation provides a great opportunity for system administrators to more easily construct and managing their systems through flexible software layers Software Defined Systems include Software Defined Networking (SDN), Software Defined Storage, Software Defined Servers (Virtualization), Software Defined Datacenters (SDD), Software Defined Security (SDSec), and ultimately Software Defined Clouds (SDCloud) to name a few possibilities Individual solutions and seamless integration of these abstractions remains in many respects a challenge |
| cold start problem in recommender systems: The Cold Start Problem Andrew Chen, 2021-12-07 'A true Silicon Valley insider' Wired Why do some products take off? And what can we learn from them? The hardest part of launching a product is getting started. When you have just an idea and a handful of customers, growth can feel impossible. This is the cold start problem. Now, one of Silicon Valley's most esteemed investors uncovers how any product can surmount the cold start problem - by harnessing the hidden power of network effects. Drawing on interviews with the founders of Uber, LinkedIn, Airbnb and Zoom, Andrew Chen reveals how any start-up can launch, scale and thrive. _ 'Chen walks readers through interviews with 30 world-class teams and founders, including from Twitch, Airbnb and Slack, to paint a picture of what it takes to turn a start-up into a massive brand' TechCrunch 'Articulates the stages that every product must go through to be successful . . . and illustrates what companies need to do to achieve them' Forbes |
| cold start problem in recommender systems: Handbook of Research on Advancements of Swarm Intelligence Algorithms for Solving Real-World Problems Cheng, Shi, Shi, Yuhui, 2020-04-24 The use of optimization algorithms has seen an emergence in various professional fields due to its ability to process data and information in an efficient and productive manner. Combining computational intelligence with these algorithms has created a trending subject of research on how much more beneficial intelligent-inspired algorithms can be within companies and organizations. As modern theories and applications are continually being developed in this area, professionals are in need of current research on how intelligent algorithms are advancing in the real world. TheHandbook of Research on Advancements of Swarm Intelligence Algorithms for Solving Real-World Problems is a pivotal reference source that provides vital research on the development of swarm intelligence algorithms and their implementation into current issues. While highlighting topics such as multi-agent systems, bio-inspired computing, and evolutionary programming, this publication explores various concepts and theories of swarm intelligence and outlines future directions of development. This book is ideally designed for IT specialists, researchers, academicians, engineers, developers, practitioners, and students seeking current research on the real-world applications of intelligent algorithms. |
| cold start problem in recommender systems: 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. |
| cold start problem in recommender systems: Encyclopedia of Machine Learning Claude Sammut, Geoffrey I. Webb, 2011-03-28 This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references. |
| cold start problem in recommender systems: Knowledge-Based Systems Rajendra Akerkar, Priti Sajja, 2009-08-25 A knowledge-based system (KBS) is a system that uses artificial intelligence techniques in problem-solving processes to support human decision-making, learning, and action. Ideal for advanced-undergraduate and graduate students, as well as business professionals, this text is designed to help users develop an appreciation of KBS and their architecture and understand a broad variety of knowledge-based techniques for decision support and planning. It assumes basic computer science skills and a math background that includes set theory, relations, elementary probability, and introductory concepts of artificial intelligence. Each of the 12 chapters is designed to be modular, providing instructors with the flexibility to model the book to their own course needs. Exercises are incorporated throughout the text to highlight certain aspects of the material presented and to simulate thought and discussion. A comprehensive text and resource, Knowledge-Based Systems provides access to the most current information in KBS and new artificial intelligences, as well as neural networks, fuzzy logic, genetic algorithms, and soft systems. |
| cold start problem in recommender systems: Social Media Mining Reza Zafarani, Mohammad Ali Abbasi, Huan Liu, 2014-04-28 Integrates social media, social network analysis, and data mining to provide an understanding of the potentials of social media mining. |
| cold start problem in recommender systems: 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. |
| cold start problem in recommender systems: Advances in Computer Communication and Computational Sciences Sanjiv K. Bhatia, Shailesh Tiwari, Krishn K. Mishra, Munesh C. Trivedi, 2018-08-22 The book includes the insights that reflect ‘Advances in Computer and Computational Sciences’ from upcoming researchers and leading academicians across the globe. It contains the high-quality peer-reviewed papers of ‘International Conference on Computer, Communication and Computational Sciences (IC4S 2017), held during 11–12 October, 2017 in Thailand. These papers are arranged in the form of chapters. The content of this book is divided into two volumes that cover variety of topics such as intelligent hardware and software design, advanced communications, intelligent computing techniques, intelligent image processing, and web and informatics. This book helps the perspective readers’ from computer industry and academia to derive the advances of next generation computer and communication technology and shape them into real life applications. |
| cold start problem in recommender systems: Developments Of Artificial Intelligence Technologies In Computation And Robotics - Proceedings Of The 14th International Flins Conference (Flins 2020) Zhong Li, Chunrong Yuan, Jie Lu, Etienne E Kerre, 2020-08-04 FLINS, an acronym introduced in 1994 and originally for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended into a well-established international research forum to advance the foundations and applications of computational intelligence for applied research in general and for complex engineering and decision support systems.The principal mission of FLINS is bridging the gap between machine intelligence and real complex systems via joint research between universities and international research institutions, encouraging interdisciplinary research and bringing multidiscipline researchers together.FLINS 2020 is the fourteenth in a series of conferences on computational intelligence systems. |
| cold start problem in recommender systems: Deep Learning Techniques and Optimization Strategies in Big Data Analytics Thomas, J. Joshua, Karagoz, Pinar, Ahamed, B. Bazeer, Vasant, Pandian, 2019-11-29 Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry. |
| cold start problem in recommender systems: Future Data and Security Engineering Tran Khanh Dang, Roland Wagner, Josef Küng, Nam Thoai, Makoto Takizawa, Erich J. Neuhold, 2017-11-20 This book constitutes the refereed proceedings of the Third International Conference on Future Data and Security Engineering, FDSE 2016, held in Can Tho City, Vietnam, in November 2016. The 28 revised full papers and 7 short papers presented were carefully reviewed and selected from 128 submissions. The accepted papers were grouped into the following sessions: Advances in query processing and optimization Big data analytics and applications Blockchains and emerging authentication techniques Data engineering tools in software development Data protection, data hiding, and access control Internet of Things and applications Security and privacy engineering Social network data analytics and recommendation systems |
| cold start problem in recommender systems: Information Science and Applications (ICISA) 2016 Kuinam J. Kim, Nikolai Joukov, 2016-02-15 This book contains selected papers from the 7th International Conference on Information Science and Applications (ICISA 2016) and provides a snapshot of the latest issues encountered in technical convergence and convergences of security technology. It explores how information science is core to most current research, industrial and commercial activities and consists of contributions covering topics including Ubiquitous Computing, Networks and Information Systems, Multimedia and Visualization, Middleware and Operating Systems, Security and Privacy, Data Mining and Artificial Intelligence, Software Engineering, and Web Technology. The contributions describe the most recent developments in information technology and ideas, applications and problems related to technology convergence, illustrated through case studies, and reviews converging existing security techniques. Through this volume, readers will gain an understanding of the current state-of-the-art information strategies and technologies of convergence security. The intended readers are researchers in academia, industry and other research institutes focusing on information science and technology. |
| cold start problem in recommender systems: R: Unleash Machine Learning Techniques Raghav Bali, Dipanjan Sarkar, Brett Lantz, Cory Lesmeister, 2016-10-24 Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner. About This Book Build your confidence with R and find out how to solve a huge range of data-related problems Get to grips with some of the most important machine learning techniques being used by data scientists and analysts across industries today Don't just learn – apply your knowledge by following featured practical projects covering everything from financial modeling to social media analysis Who This Book Is For Aimed for intermediate-to-advanced people (especially data scientist) who are already into the field of data science What You Will Learn Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action Solve interesting real-world problems using machine learning and R as the journey unfolds Write reusable code and build complete machine learning systems from the ground up Learn specialized machine learning techniques for text mining, social network data, big data, and more Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems Evaluate and improve the performance of machine learning models Learn specialized machine learning techniques for text mining, social network data, big data, and more In Detail R is the established language of data analysts and statisticians around the world. And you shouldn't be afraid to use it... This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R. In the first module you'll get to grips with the fundamentals of R. This means you'll be taking a look at some of the details of how the language works, before seeing how to put your knowledge into practice to build some simple machine learning projects that could prove useful for a range of real world problems. For the following two modules we'll begin to investigate machine learning algorithms in more detail. To build upon the basics, you'll get to work on three different projects that will test your skills. Covering some of the most important algorithms and featuring some of the most popular R packages, they're all focused on solving real problems in different areas, ranging from finance to social media. This Learning Path has been curated from three Packt products: R Machine Learning By Example By Raghav Bali, Dipanjan Sarkar Machine Learning with R Learning - Second Edition By Brett Lantz Mastering Machine Learning with R By Cory Lesmeister Style and approach This is an enticing learning path that starts from the very basics to gradually pick up pace as the story unfolds. Each concept is first defined in the larger context of things succinctly, followed by a detailed explanation of their application. Each topic is explained with the help of a project that solves a real-world problem involving hands-on work thus giving you a deep insight into the world of machine learning. |
| cold start problem in recommender systems: 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 |
| cold start problem in recommender systems: Data Mining and Machine Learning Applications Rohit Raja, Kapil Kumar Nagwanshi, Sandeep Kumar, K. Ramya Laxmi, 2022-03-02 DATA MINING AND MACHINE LEARNING APPLICATIONS The book elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration. Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data. Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth. The book features: A review of the state-of-the-art in data mining and machine learning, A review and description of the learning methods in human-computer interaction, Implementation strategies and future research directions used to meet the design and application requirements of several modern and real-time applications for a long time, The scope and implementation of a majority of data mining and machine learning strategies. A discussion of real-time problems. Audience Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly. |
| cold start problem in recommender systems: 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 |
| cold start problem in recommender systems: ICT Innovations 2017 Dimitar Trajanov, Verica Bakeva, 2017-09-06 This book constitutes the refereed proceedings of the 9th International Conference on Data-Driven Innovation, ICT Innovations 2017, held in Skopje, Macedonia, in September 2017. The 26 full papers presented were carefully reviewed and selected from 90 submissions. They cover the following topics: big data analytics, cloud computing, data mining, digital signal processing, e-health, embedded systems, emerging mobile technologies, multimedia, Internet of Things (IoT), machine learning, software engineering, security and cryptography, coding theory, wearable technologies, wireless communication, and sensor networks. |
| cold start problem in recommender systems: Data Analysis, Machine Learning and Applications Christine Preisach, Hans Burkhardt, Lars Schmidt-Thieme, Reinhold Decker, 2008-04-13 Data analysis and machine learning are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medical science, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and applications presented during the 31st Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl). The conference was held at the Albert-Ludwigs-University in Freiburg, Germany, in March 2007. |
| cold start problem in recommender systems: 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. |
| cold start problem in recommender systems: Destination Recommendation Systems Daniel R. Fesenmaier, Karl W. Wöber, H. Werthner, 2006-01-01 Bringing together the work of leading researchers, this book provides a clear and accessible overview of current research on destination recommendation systems. These systems guide consumer behaviour by enabling Internet users to quickly and effectively find relevant information about travel destinations, attractions, accommodation and transportation. The chapters in this book cover consumer behaviour, perceptual factors influencing consumer choice, and the design of destination recommendation systems. The book examines four different types of destination marketing system and concludes by analysing the future of recommendation systems for travellers. |
| cold start problem in recommender systems: User Modeling 2007 Cristina Conati, Kathleen McCoy, Georgios Paliouras, 2007-08-28 This book constitutes the refereed proceedings of the 11th International Conference on User Modeling, UM 2007, held in Corfu, Greece in July 2007. Coverage includes evaluating user/student modeling techniques, data mining and machine learning for user modeling, user adaptation and usability, modeling affect and meta-cognition, as well as intelligent information retrieval, information filtering and content personalization. |
| cold start problem in recommender systems: Information and Communication Technology for Sustainable Development Durgesh Kumar Mishra, Malaya Kumar Nayak, Amit Joshi, 2018 The book proposes new technologies and discusses future solutions for design infrastructure for ICT. The book contains high quality submissions presented at Second International Conference on Information and Communication Technology for Sustainable Development (ICT4SD - 2016) held at Goa, India during 1 - 2 July, 2016. The conference stimulates the cutting-edge research discussions among many academic pioneering researchers, scientists, industrial engineers, and students from all around the world. The topics covered in this book also focus on innovative issues at international level by bringing together the experts from different countries. |
| cold start problem in recommender systems: Explainable Recommendation Yongfeng Zhang, Xu Chen, 2020-03-10 In recent years, a large number of explainable recommendation approaches have been proposed and applied in real-world systems. This survey provides a comprehensive review of the explainable recommendation research. |
| cold start problem in recommender systems: Big Data Recommender Systems Osman Khalid, Samee U. Khan, Albert Y. Zomaya, 2019-07-04 First designed to generate personalized recommendations to users in the 90s, recommender systems apply knowledge discovery techniques to users’ data to suggest information, products, and services that best match their preferences. In recent decades, we have seen an exponential increase in the volumes of data, which has introduced many new challenges. |
| cold start problem in recommender systems: Computational Science – ICCS 2021 Maciej Paszynski, Dieter Kranzlmüller, Valeria V. Krzhizhanovskaya, Jack J. Dongarra, Peter M. A. Sloot, 2021-06-11 The six-volume set LNCS 12742, 12743, 12744, 12745, 12746, and 12747 constitutes the proceedings of the 21st International Conference on Computational Science, ICCS 2021, held in Krakow, Poland, in June 2021.* The total of 260 full papers and 57 short papers presented in this book set were carefully reviewed and selected from 635 submissions. 48 full and 14 short papers were accepted to the main track from 156 submissions; 212 full and 43 short papers were accepted to the workshops/ thematic tracks from 479 submissions. The papers were organized in topical sections named: Part I: ICCS Main Track Part II: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Artificial Intelligence and High-Performance Computing for Advanced Simulations; Biomedical and Bioinformatics Challenges for Computer Science Part III: Classifier Learning from Difficult Data; Computational Analysis of Complex Social Systems; Computational Collective Intelligence; Computational Health Part IV: Computational Methods for Emerging Problems in (dis-)Information Analysis; Computational Methods in Smart Agriculture; Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems Part V: Computer Graphics, Image Processing and Artificial Intelligence; Data-Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; MeshFree Methods and Radial Basis Functions in Computational Sciences; Multiscale Modelling and Simulation Part VI: Quantum Computing Workshop; Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainty; Teaching Computational Science; Uncertainty Quantification for Computational Models *The conference was held virtually. Chapter “Effective Solution of Ill-posed Inverse Problems with Stabilized Forward Solver” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com. |
| cold start problem in recommender systems: 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. |
| cold start problem in recommender systems: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems Alexandros Karatzoglou, 2016-09-15 Workshop on Deep Learning for Recommender Systems Sep 15, 2016-Sep 15, 2016 Boston, USA. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl. |
| cold start problem in recommender systems: Introduction to Modern Information Retrieval Gerard Salton, Michael J. McGill, 1983 Examines Concepts, Functions & Processes of Information Retrieval Systems |
| cold start problem in recommender systems: Social Network Based Big Data Analysis and Applications Mehmet Kaya, Jalal Kawash, Suheil Khoury, Min-Yuh Day, 2018-06-24 This book is a timely collection of chapters that present the state of the art within the analysis and application of big data. Working within the broader context of big data, this text focuses on the hot topics of social network modelling and analysis such as online dating recommendations, hiring practices, and subscription-type prediction in mobile phone services. Manuscripts are expanded versions of the best papers presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’2016), which was held in August 2016. The papers were among the best featured at the meeting and were then improved and extended substantially. Social Network Based Big Data Analysis and Applications will appeal to students and researchers in the field. |
| cold start problem in recommender systems: Managing the Web of Things Michael Sheng, Yongrui Qin, Boualem Benatallah, Lina Yao, 2017-02-01 Managing the Web of Things: Linking the Real World to the Web presents a consolidated and holistic coverage of engineering, management, and analytics of the Internet of Things. The web has gone through many transformations, from traditional linking and sharing of computers and documents (i.e., Web of Data), to the current connection of people (i.e., Web of People), and to the emerging connection of billions of physical objects (i.e., Web of Things). With increasing numbers of electronic devices and systems providing different services to people, Web of Things applications present numerous challenges to research institutions, companies, governments, international organizations, and others. This book compiles the newest developments and advances in the area of the Web of Things, ranging from modeling, searching, and data analytics, to software building, applications, and social impact. Its coverage will enable effective exploration, understanding, assessment, comparison, and the selection of WoT models, languages, techniques, platforms, and tools. Readers will gain an up-to-date understanding of the Web of Things systems that accelerates their research. Offers a comprehensive and systematic presentation of the methodologies, technologies, and applications that enable efficient and effective management of the Internet of Things Provides an in-depth analysis on the state-of-the-art Web of Things modeling and searching technologies, including how to collect, clean, and analyze data generated by the Web of Things Covers system design and software building principles, with discussions and explorations of social impact for the Web of Things through real-world applications Acts as an ideal reference or recommended text for graduate courses in cloud computing, service computing, and more |
| cold start problem in recommender systems: Collaborative Filtering [microform] : a Machine Learning Perspective Benjamin Marlin, 2004 Collaborative filtering was initially proposed as a framework for filtering information based on the preferences of users, and has since been refined in many different ways. This thesis is a comprehensive study of rating-based, pure, non-sequential collaborative filtering. We analyze existing methods for the task of rating prediction from a machine learning perspective. We show that many existing methods proposed for this task are simple applications or modifications of one or more standard machine learning methods for classification, regression, clustering, dimensionality reduction, and density estimation. We introduce new prediction methods in all of these classes. We introduce a new experimental procedure for testing stronger forms of generalization than has been used previously. We implement a total of nine prediction methods, and conduct large scale prediction accuracy experiments. We show interesting new results on the relative performance of these methods. |
| cold start problem in recommender systems: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Longbing Cao, 2015 |
| cold start problem in recommender systems: KDD2019 , 2019 |
Common cold - Symptoms and causes - Mayo Clinic
May 24, 2023 · Typical signs and symptoms include earaches or the return of a fever following a common cold. Asthma. A cold can trigger wheezing, even in people who don't have asthma. …
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Jul 12, 2024 · Cold and cough medicines in young children. Cold and cough medicine you can get without a prescription can harm children. Do not give any cough and cold medicines to …
Common cold - Diagnosis and treatment - Mayo Clinic
May 24, 2023 · The science isn't clear on alternative cold remedies such as vitamin C, echinacea and zinc. Because studies of alternative cold remedies in children are limited, these remedies …
COVID-19, cold, allergies and the flu: What are the differences?
Nov 27, 2024 · There's no cure for the common cold. Treatment may include pain relievers and cold remedies available without a prescription, such as decongestants. Unlike COVID-19, a …
Common cold in babies - Symptoms & causes - Mayo Clinic
Apr 11, 2025 · A common cold can cause: Acute ear infection, called otitis media. This is the most common complication of the common cold. Ear infections occur when bacteria or viruses enter …
Mayo Clinic Q and A: Myths about catching a cold
Feb 10, 2022 · Cold ice cream can soothe a sore throat, and probiotics in yogurt can help alleviate stomach upset if you are taking antibiotics for an infection. Check with your primary health …
Cold or allergy: Which is it? - Mayo Clinic
Feb 13, 2024 · A cold may last 3 to 10 days in adults, although a cough may last for a couple of weeks longer. You can treat the symptoms of the common cold with rest and added fluids. …
What to do if you get a respiratory infection: A Mayo Clinic …
Dec 30, 2024 · Flu symptoms include sore throat, runny or stuffy nose, fever, body aches and fatigue. These symptoms may overlap with other illnesses, like the common cold, but key …
Cold urticaria - Symptoms & causes - Mayo Clinic
May 6, 2025 · Cold urticaria (ur-tih-KAR-e-uh) is a reaction that appears within minutes after skin is exposed to the cold. Itchy welts, also called hives, arise on affected skin. Symptoms in …
Cold sore - Diagnosis and treatment - Mayo Clinic
Jan 5, 2024 · The cold sore ointment docosanol (Abreva) may shorten the healing time of a cold sore. At the first sign of symptoms, apply it to the affected skin as directed on the package. …
Common cold - Symptoms and causes - Mayo Clinic
May 24, 2023 · Typical signs and symptoms include earaches or the return of a fever following a common cold. Asthma. A cold can trigger wheezing, even in people who don't have asthma. …
Cold remedies: What works, what doesn't, what can't hurt
Jul 12, 2024 · Cold and cough medicines in young children. Cold and cough medicine you can get without a prescription can harm children. Do not give any cough and cold medicines to …
Common cold - Diagnosis and treatment - Mayo Clinic
May 24, 2023 · The science isn't clear on alternative cold remedies such as vitamin C, echinacea and zinc. Because studies of alternative cold remedies in children are limited, these remedies …
COVID-19, cold, allergies and the flu: What are the differences?
Nov 27, 2024 · There's no cure for the common cold. Treatment may include pain relievers and cold remedies available without a prescription, such as decongestants. Unlike COVID-19, a …
Common cold in babies - Symptoms & causes - Mayo Clinic
Apr 11, 2025 · A common cold can cause: Acute ear infection, called otitis media. This is the most common complication of the common cold. Ear infections occur when bacteria or viruses enter …
Mayo Clinic Q and A: Myths about catching a cold
Feb 10, 2022 · Cold ice cream can soothe a sore throat, and probiotics in yogurt can help alleviate stomach upset if you are taking antibiotics for an infection. Check with your primary health …
Cold or allergy: Which is it? - Mayo Clinic
Feb 13, 2024 · A cold may last 3 to 10 days in adults, although a cough may last for a couple of weeks longer. You can treat the symptoms of the common cold with rest and added fluids. …
What to do if you get a respiratory infection: A Mayo Clinic …
Dec 30, 2024 · Flu symptoms include sore throat, runny or stuffy nose, fever, body aches and fatigue. These symptoms may overlap with other illnesses, like the common cold, but key …
Cold urticaria - Symptoms & causes - Mayo Clinic
May 6, 2025 · Cold urticaria (ur-tih-KAR-e-uh) is a reaction that appears within minutes after skin is exposed to the cold. Itchy welts, also called hives, arise on affected skin. Symptoms in …
Cold sore - Diagnosis and treatment - Mayo Clinic
Jan 5, 2024 · The cold sore ointment docosanol (Abreva) may shorten the healing time of a cold sore. At the first sign of symptoms, apply it to the affected skin as directed on the package. …
