The Experts below are selected from a list of 53745 Experts worldwide ranked by ideXlab platform
Francesco Ricci - One of the best experts on this subject based on the ideXlab platform.
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Recommender Systems Handbook - Context-Aware Recommender Systems
Ai Magazine, 2011Co-Authors: Gediminas Adomavicius, Francesco Ricci, Bamshad Mobasher, Alexander TuzhilinAbstract:Context-aware Recommender Systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create more intelligent and useful Recommender Systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges and future research directions for context-aware Recommender Systems.
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Recommender Systems Handbook - Recommender Systems Handbook
2010Co-Authors: Francesco Ricci, Lior Rokach, Bracha Shapira, Paul B. KantorAbstract:The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender Systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments. Recommender Systems Handbook, an edited volume, 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. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate Recommender Systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of Recommender Systems major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included. Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference.
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Recommender Systems handbook
rsh, 2010Co-Authors: Francesco Ricci, Lior Rokach, Bracha Shapira, Paul B. KantorAbstract:The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender Systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments. Recommender Systems Handbook, an edited volume, 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. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate Recommender Systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of Recommender Systems major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included. Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference.
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mobile Recommender Systems
Information Technology & Tourism, 2010Co-Authors: Francesco RicciAbstract:Mobile phones are becoming a primary platform for information access and when coupled with Recommender Systems technologies they can become key tools for mobile users both for leisure and business applications. Recommendation techniques can increase the usability of mobile Systems providing personalized and more focussed content, hence limiting the negative effects of information overload. In this paper we review the major issues and opportunities that the mobile scenario opens to the application of Recommender Systems especially in the area of travel and tourism. We overview major techniques that have been proposed in the last years and we illustrate the supported functions. We also illustrate specific computational models that have been proposed for mobile Recommender Systems and we close the paper by presenting some possible future developments and extension in this area.
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Recommender Systems Handbook
Annals of Physics, 2010Co-Authors: Paul B. Kantor, Lior Rokach, Francesco Ricci, Bracha ShapiraAbstract:The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious, users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender Systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments.Recommender Systems Handbook, an edited volume, 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. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate Recommender Systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of Recommender Systems' major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included. Recommender Systems Handbook supports the user in decision-making, planning and purchasing processes who work for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is also suitable for researchers and advanced-level students in computer science as a reference.
Katrien Verbert - One of the best experts on this subject based on the ideXlab platform.
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interactive Recommender Systems
Expert Systems With Applications, 2016Co-Authors: Chen He, Denis Parra, Katrien VerbertAbstract:We identify shortcomings of current Recommender Systems.We present an interactive Recommender framework to tackle the shortcomings.We analyze existing interactive Recommenders along the dimensions of our framework.Based on the analysis, we identify future research challenges and opportunities. Recommender Systems have been researched extensively over the past decades. Whereas several algorithms have been developed and deployed in various application domains, recent research efforts are increasingly oriented towards the user experience of Recommender Systems. This research goes beyond accuracy of recommendation algorithms and focuses on various human factors that affect acceptance of recommendations, such as user satisfaction, trust, transparency and sense of control. In this paper, we present an interactive visualization framework that combines recommendation with visualization techniques to support human-Recommender interaction. Then, we analyze existing interactive Recommender Systems along the dimensions of our framework, including our work. Based on our survey results, we present future research challenges and opportunities.
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HCI for Recommender Systems
Proceedings of the 10th ACM Conference on Recommender Systems - RecSys '16, 2016Co-Authors: André Calero Valdez, Martina Ziefle, Katrien VerbertAbstract:How can you discover something new, that matches your in- terest? Recommender Systems have been studied since the 90ies. Their benefit comes from guiding a user through the density of the information jungle to useful knowledge clear- ings. Early research on Recommender Systems focuses on al- gorithms and their evaluation to improve recommendation accuracy using F-measures and other methodologies from signal-detection theory. Present research includes other as- pects such as human factors that affect the user experience and interactive visualization techniques to support trans- parency of results and user control. In this paper, we ana- lyze all publications on Recommender Systems from the sco- pus database, and particularly also papers with such an HCI focus. Based on an analysis of these papers, future topics for Recommender Systems research are identified, which include more advanced support for user control, adaptive interfaces, affective computing and applications in high risk domains. Keywords
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Recommender Systems Handbook - Panorama of Recommender Systems to Support Learning
Recommender Systems Handbook, 2015Co-Authors: Hendrik Drachsler, Olga C. Santos, Katrien Verbert, Nikos ManouselisAbstract:This chapter presents an analysis of Recommender Systems in Technology-Enhanced Learning along their 15 years existence (2000–2014). All Recommender Systems considered for the review aim to support educational stakeholders by personalising the learning process. In this meta-review 82 Recommender Systems from 35 different countries have been investigated and categorised according to a given classification framework. The reviewed Systems have been classified into seven clusters according to their characteristics and analysed for their contribution to the evolution of the RecSysTEL research field. Current challenges have been identified to lead the work of the forthcoming years.
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Recommender Systems Handbook
Recommender Systems Handbook, 2015Co-Authors: Hendrik Drachsler, Olga C. Santos, Katrien Verbert, Nikos ManouselisAbstract:This chapter presents an analysis of Recommender Systems in Technology- Enhanced Learning along their 15 years existence (2000-2014). All Recommender Systems considered for the review aim to support educational stakeholders by per- sonalising the learning process. In this meta-review 82 Recommender Systems from 35 different countries have been investigated and categorised according to a given classification framework. The reviewed Systems have been classified into 7 clusters according to their characteristics and analysed for their contribution to the evolution of the RecSysTEL research field. Current challenges have been identified to lead the work of the forthcoming years.
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Recommender Systems for Learning
SpringerBriefs in Electrical and Computer Engineering, 2013Co-Authors: Nikos Manouselis, Hendrik Drachsler, Katrien Verbert, Erik DuvalAbstract:Technology enhanced learning (TEL) aims to design; as well as to highlight their particularities compared to Recommender Systems for other application domains.; develop and test sociotechnical innovations that will support and enhance learning practices of both individuals and organisations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learning activities. Since information retrieval (in terms of searching for relevant learning resources to support teachers or learners) is a pivotal activity in TEL; the deployment of Recommender Systems has attracted increased interest. This brief attempts to provide an introduction to Recommender Systems for TEL settings.
Balázs Hidasi - One of the best experts on this subject based on the ideXlab platform.
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RecSys - Deep Learning for Recommender Systems
Proceedings of the Eleventh ACM Conference on Recommender Systems - RecSys '17, 2017Co-Authors: Alexandros Karatzoglou, Balázs HidasiAbstract:Deep Learning is one of the next big things in Recommendation Systems technology. The past few years have seen the tremendous success of deep neural networks in a number of complex machine learning tasks such as computer vision, natural language processing and speech recognition. After its relatively slow uptake by the Recommender Systems community, deep learning for Recommender Systems became widely popular in 2016. We believe that a tutorial on the topic of deep learning will do its share to further popularize the topic. Notable recent application areas are music recommendation, news recommendation, and session-based recommendation. The aim of the tutorial is to encourage the application of Deep Learning techniques in Recommender Systems, to further promote research in deep learning methods for Recommender Systems.
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Deep Learning for Recommender Systems
Proceedings of the Eleventh ACM Conference on Recommender Systems - RecSys '17, 2017Co-Authors: Alexandros Karatzoglou, Balázs HidasiAbstract:Deep Learning is one of the next big things in Recommendation Systems technology. The past few years have seen the tremendous success of deep neural networks in a number of complex machine learning tasks such as computer vision, natural language processing and speech recognition. After its relatively slow uptake by the Recommender Systems community, deep learning for Recommender Systems became widely popular in 2016. We believe that a tutorial on the topic of deep learning will do its share to further popularize the topic. Notable recent application areas are music recommendation, news recommendation, and session-based recommendation. The aim of the tutorial is to encourage the application of Deep Learning techniques in Recommender Systems, to further promote research in deep learning methods for Recommender Systems.
Gediminas Adomavicius - One of the best experts on this subject based on the ideXlab platform.
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Recommender Systems Handbook - Multi-Criteria Recommender Systems
Springer US, 2020Co-Authors: Gediminas Adomavicius, Youngok KwonAbstract:This chapter aims to provide an overview of the class of multi-criteria Recommender Systems, i.e., the category of Recommender Systems that use multi-criteria preference ratings. Traditionally, the vast majority of Recommender Systems literature has focused on providing recommendations by modelling a user’s utility (or preference) for an item as a single preference rating. However, where possible, capturing richer user preferences along several dimensions—for example, capturing not only the user’s overall preference for a given movie but also her preferences for specific movie aspects (such as acting, story, or visual effects)—can provide opportunities for further improvements in recommendation quality. As a result, a number of recommendation techniques that attempt to take advantage of such multi-criteria preference information have been developed in recent years. A review of current algorithms that use multi-criteria ratings for calculating predictions and generating recommendations is provided. The chapter concludes with a discussion on open issues and future challenges for the class of multi-criteria rating Recommenders.
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Context-aware Recommender Systems
Recommender Systems Handbook Second Edition, 2015Co-Authors: Gediminas Adomavicius, Alexander TuzhilinAbstract:Context-aware Recommender Systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create more intelligent and useful Recommender Systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges and future research directions for context-aware Recommender Systems.
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multi criteria Recommender Systems
Springer US, 2015Co-Authors: Gediminas Adomavicius, Youngok KwonAbstract:This chapter aims to provide an overview of the class of multi-criteria Recommender Systems, i.e., the category of Recommender Systems that use multi-criteria preference ratings. Traditionally, the vast majority of Recommender Systems literature has focused on providing recommendations by modelling a user’s utility (or preference) for an item as a single preference rating. However, where possible, capturing richer user preferences along several dimensions—for example, capturing not only the user’s overall preference for a given movie but also her preferences for specific movie aspects (such as acting, story, or visual effects)—can provide opportunities for further improvements in recommendation quality. As a result, a number of recommendation techniques that attempt to take advantage of such multi-criteria preference information have been developed in recent years. A review of current algorithms that use multi-criteria ratings for calculating predictions and generating recommendations is provided. The chapter concludes with a discussion on open issues and future challenges for the class of multi-criteria rating Recommenders.
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Recommender Systems Handbook - Context-Aware Recommender Systems
Ai Magazine, 2011Co-Authors: Gediminas Adomavicius, Francesco Ricci, Bamshad Mobasher, Alexander TuzhilinAbstract:Context-aware Recommender Systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create more intelligent and useful Recommender Systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges and future research directions for context-aware Recommender Systems.
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Personalization and Recommender Systems
State-of-the-Art Decision-Making Tools in the Information-Intensive Age, 2008Co-Authors: Gediminas Adomavicius, Zan Huang, Alexander TuzhilinAbstract:In this tutorial, we present an overview of the personalization field and review dif- ferent types of personalization. We also discuss the general personalization process and position the field of Recommender Systems as an integral part of this process. We review the field of Recommender Systems by describing a number of “traditional” recommendation approaches and their extensions. Finally, we discuss several future research directions for personalization and Recommender Systems, including integrated personalization process, data acquisition for Recommender Systems, advanced model- ing of user preferences, other model-based techniques for recommendation, evaluation of Recommender Systems, recommendation flexibility and scalability, and trust and privacy issues in Recommender Systems.
George A. Papadopoulos - One of the best experts on this subject based on the ideXlab platform.
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Ubiquitous Recommender Systems
Computing, 2014Co-Authors: Christos Mettouris, George A. PapadopoulosAbstract:Ubiquitous Recommender Systems combine characteristics from ubiquitous Systems and Recommender Systems in order to provide personalized recommendations to users in ubiquitous environments. Although not a new research area, ubiquitous Recommender Systems research has not yet been reviewed and classified in terms of ubiquitous research and Recommender Systems research, in order to deeply comprehend its nature, characteristics, relevant issues and challenges. It is our belief that ubiquitous Recommenders can nowadays take advantage of the progress mobile phone technology has made in identifying items around, as well as utilize the faster wireless connections and the endless capabilities of modern mobile devices in order to provide users with more personalized and context-aware recommendations on location to aid them with their task at hand. This work focuses on ubiquitous Recommender Systems, while a brief analysis of the two fundamental areas from which they emerged, ubiquitous computing and Recommender Systems research is also conducted. Related work is provided, followed by a classification schema and a discussion about the correlation of ubiquitous Recommenders with classic ubiquitous Systems and Recommender Systems: similarities inevitably exist, however their fundamental differences are crucial. The paper concludes by proposing UbiCARS: a new class of ubiquitous Recommender Systems that will combine characteristics from ubiquitous Systems and context-aware Recommender Systems in order to utilize multidimensional context modeling techniques not previously met in ubiquitous Recommender Systems. [ABSTRACT FROM AUTHOR] Copyright of Computing is the property of Springer Science & Business Media B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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Ubiquitous Recommender Systems
Computing, 2013Co-Authors: Christos Mettouris, George A. PapadopoulosAbstract:Ubiquitous Recommender Systems combine characteristics from ubiquitous Systems and Recommender Systems in order to provide personalized recommendations to users in ubiquitous environments. Although not a new research area, ubiquitous Recommender Systems research has not yet been reviewed and classified in terms of ubiquitous research and Recommender Systems research, in order to deeply comprehend its nature, characteristics, relevant issues and challenges. It is our belief that ubiquitous Recommenders can nowadays take advantage of the progress mobile phone technology has made in identifying items around, as well as utilize the faster wireless connections and the endless capabilities of modern mobile devices in order to provide users with more personalized and context-aware recommendations on location to aid them with their task at hand. This work focuses on ubiquitous Recommender Systems, while a brief analysis of the two fundamental areas from which they emerged, ubiquitous computing and Recommender Systems research is also conducted. Related work is provided, followed by a classification schema and a discussion about the correlation of ubiquitous Recommenders with classic ubiquitous Systems and Recommender Systems: similarities inevitably exist, however their fundamental differences are crucial. The paper concludes by proposing UbiCARS: a new class of ubiquitous Recommender Systems that will combine characteristics from ubiquitous Systems and context-aware Recommender Systems in order to utilize multidimensional context modeling techniques not previously met in ubiquitous Recommender Systems.
