Recommender Systems: Introduction and Challenges

Recommender Systems (RSs) are software tools and techniques that provide suggestions for items that are most likely of interest to a particular user. In this introductory chapter, we briefly discuss basic RS ideas and concepts. Our main goal is to delineate, in a coherent and structured way, the chapters included in this handbook. Additionally, we aim to help the reader navigate the rich and detailed content that this handbook offers.

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[8]  Amnon Meisels,et al.  Recommender System from Personal Social Networks , 2007, AWIC.

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[10]  Antonio Moreno,et al.  Intelligent tourism recommender systems: A survey , 2014, Expert Syst. Appl..

[11]  Francesco Ricci,et al.  Experimental evaluation of context-dependent collaborative filtering using item splitting , 2013, User Modeling and User-Adapted Interaction.

[12]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

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[18]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.

[19]  Bart P. Knijnenburg,et al.  Explaining the user experience of recommender systems , 2012, User Modeling and User-Adapted Interaction.

[20]  F. Maxwell Harper,et al.  User perception of differences in recommender algorithms , 2014, RecSys '14.

[21]  Erik Duval,et al.  Dataset-driven research for improving recommender systems for learning , 2011, LAK.

[22]  John Riedl,et al.  Explaining collaborative filtering recommendations , 2000, CSCW '00.

[23]  Tsvi Kuflik,et al.  Mediation of user models for enhanced personalization in recommender systems , 2007, User Modeling and User-Adapted Interaction.

[24]  Dan Frankowski,et al.  Collaborative Filtering Recommender Systems , 2007, The Adaptive Web.

[25]  Pasquale Lops,et al.  Content-based Recommender Systems: State of the Art and Trends , 2011, Recommender Systems Handbook.

[26]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[27]  Xavier Amatriain,et al.  Mining large streams of user data for personalized recommendations , 2013, SKDD.

[28]  Ofer Arazy,et al.  Improving Social Recommender Systems , 2009, IT Professional.

[29]  Daniel Billsus,et al.  Learning Probabilistic User Models , 1998 .

[30]  Robin D. Burke,et al.  Hybrid Web Recommender Systems , 2007, The Adaptive Web.

[31]  Saeed Shiry Ghidary,et al.  Usage-based web recommendations: a reinforcement learning approach , 2007, RecSys '07.

[32]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[33]  Gerhard Fischer,et al.  User Modeling in Human–Computer Interaction , 2001, User Modeling and User-Adapted Interaction.

[34]  Yoav Shoham,et al.  Content-Based, Collaborative Recommendation. , 1997 .

[35]  J. Bobadilla,et al.  Recommender systems survey , 2013, Knowl. Based Syst..

[36]  Josep Lluís de la Rosa i Esteva,et al.  A Taxonomy of Recommender Agents on the Internet , 2003, Artificial Intelligence Review.

[37]  B. Ohman,et al.  Discrete sensor validation with multilevel flow models , 2002 .

[38]  Li Chen,et al.  Critiquing-based recommenders: survey and emerging trends , 2012, User Modeling and User-Adapted Interaction.

[39]  Martin P. Robillard,et al.  Recommendation Systems for Software Engineering , 2010, IEEE Software.

[40]  Sean M. McNee,et al.  Beyond personalization: the next stage of recommender systems research , 2005, IUI '05.

[41]  Francesco Ricci,et al.  Improving Recommendation Effectiveness: Adapting a Dialogue Strategy in Online Travel Planning , 2009, J. Inf. Technol. Tour..

[42]  John Riedl,et al.  Recommender systems: from algorithms to user experience , 2012, User Modeling and User-Adapted Interaction.

[43]  John Riedl,et al.  E-Commerce Recommendation Applications , 2004, Data Mining and Knowledge Discovery.

[44]  Rashmi R. Sinha,et al.  Comparing Recommendations Made by Online Systems and Friends , 2001, DELOS.

[45]  Tsvi Kuflik,et al.  Cross-representation mediation of user models , 2009, User Modeling and User-Adapted Interaction.

[46]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[47]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[48]  Barry Smyth,et al.  Case-based recommender systems , 2005, The Knowledge Engineering Review.

[49]  Jae Kyeong Kim,et al.  A literature review and classification of recommender systems research , 2012, Expert Syst. Appl..

[50]  Pasquale Lops,et al.  Human Decision Making and Recommender Systems , 2013, TIIS.

[51]  Hideki Asoh,et al.  An Acceptance Model of Recommender Systems Based on a Large-Scale Internet Survey , 2011, UMAP Workshops.

[52]  John Riedl,et al.  Is seeing believing?: how recommender system interfaces affect users' opinions , 2003, CHI '03.

[53]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[54]  Francesco Ricci,et al.  Contextual music information retrieval and recommendation: State of the art and challenges , 2012, Comput. Sci. Rev..

[55]  Sophie Ahrens,et al.  Recommender Systems , 2012 .