Recommender Systems: Introduction and Challenges Additional Noteworthy Conferences within This Scope Include: Acm's Special Interest Group on Information Retrieval (sigir); User Modeling, Adaptation and Personalization (umap); Intelligent User Interfaces (iui); World Wide Web (www); and Acm's Specia

Recommender Systems (RSs) are software tools and techniques that provide suggestions for items that are most likely of interest to a particular user [17, 41, 42]. The suggestions relate to various decision-making processes, such as what items to buy, what music to listen to, or what online news to read. “Item” is the general term used to denote what the system recommends to users. An RS normally focuses on a specific type of item (e.g., CDs or news) and, accordingly its design, its graphical user interface, and the core recommendation technique used to generate the recommendations are all customized to provide useful and effective suggestions for that specific type of item. RSs are primarily directed toward individuals who lack the sufficient personal experience or competence in order to evaluate the potentially overwhelming number of alternative items that a website, for example, may offer [42]. A prime example is a book recommender system that assists users in selecting a book to read. On the popular website, Amazon.com, the site employs an RS to personalize the online store for each customer [32]. Since recommendations are usually personalized, different users or user groups benefit from diverse, tailored suggestions. In addition, there are also non-personalized recommendations. These are much simpler to generate and are normally featured in magazines or newspapers. Typical examples

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

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

[3]  Bradley N. Miller,et al.  Social Information Filtering : Algorithms for Automating “ Word of Mouth , ” , 2017 .

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

[5]  Sean M. McNee,et al.  Being accurate is not enough: how accuracy metrics have hurt recommender systems , 2006, CHI Extended Abstracts.

[6]  Peter Brusilovsky,et al.  Methods and techniques of adaptive hypermedia , 1996, User Modeling and User-Adapted Interaction.

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

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

[9]  Kirsten Swearingen,et al.  Beyond Algorithms: An HCI Perspective on Recommender Systems , 2001 .

[10]  D. Fesenmaier,et al.  Case-based travel recommendations. , 2006 .

[11]  Francesco Ricci,et al.  Improving recommender systems with adaptive conversational strategies , 2009, HT '09.

[12]  Jennifer Golbeck,et al.  Generating Predictive Movie Recommendations from Trust in Social Networks , 2006, iTrust.

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

[14]  R. A. Bailey,et al.  Design of comparative experiments , 2008 .

[15]  Francesco Ricci,et al.  Travel Recommender Systems , 2002 .

[16]  Gerhard Friedrich,et al.  Recommender Systems - An Introduction , 2010 .

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

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

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

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

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

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

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

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

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

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

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

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

[29]  Gediminas Adomavicius,et al.  Personalization technologies: A process-oriented perspective , 2006, Wirtschaftsinf..

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

[31]  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.

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

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

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

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

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

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

[38]  Thierry Bertin-Mahieux,et al.  The million song dataset challenge , 2012, WWW.

[39]  Francesco Ricci,et al.  Recommender Systems , 2007, 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07).

[40]  Martin P. Robillard,et al.  Recommendation Systems in Software Engineering , 2014, Springer Berlin Heidelberg.

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

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

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

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

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

[46]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[47]  Manfred Jaeger,et al.  Encyclopedia of Social Network Analysis and Mining , 2014 .

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

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

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

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

[52]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

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

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