Chapter 1 Decision Tasks and Basic Algorithms

Recommender systems are decision support systems helping users to identify one or more items (solutions) that fit their wishes and needs. The most frequent application of recommender systems nowadays is to propose items to individual users. However, there are many scenarios where a group of users should receive a recommendation. For example, think of a group decision regarding the next holiday destination or a group decision regarding a restaurant to visit for a joint dinner. The goal of this book is to provide an introduction to group recommender systems, i.e., recommender systems that determine recommendations for groups. In this chapter, we provide an introduction to basic types of recommendation algorithms for individual users and characterize related decision tasks. This introduction serves as a basis for the introduction of group recommendation algorithms in Chapter 2.

[1]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[2]  J. Cacioppo,et al.  Central and Peripheral Routes to Advertising Effectiveness: The Moderating Role of Involvement , 1983 .

[3]  Raymond Reiter,et al.  A Theory of Diagnosis from First Principles , 1986, Artif. Intell..

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

[5]  Barry Nalebuff,et al.  An Introduction to Vote-Counting Schemes , 1995 .

[6]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[7]  Kristian J. Hammond,et al.  The FindMe Approach to Assisted Browsing , 1997, IEEE Expert.

[8]  Joseph F. McCarthy,et al.  MusicFX: an arbiter of group preferences for computer supported collaborative workouts , 1998, CSCW '98.

[9]  M. F. Luce,et al.  Constructive Consumer Choice Processes , 1998 .

[10]  Michael J. Pazzani,et al.  Learning Collaborative Information Filters , 1998, ICML.

[11]  Robin Burke,et al.  Knowledge-based recommender systems , 2000 .

[12]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[13]  Francesco Ricci,et al.  Case-Based Recommender Systems: A Unifying View , 2003, ITWP.

[14]  Liliana Ardissono,et al.  A Framework for the Development of Personalized, Distributed Web-Based Configuration Systems , 2003, AI Mag..

[15]  Anthony Jameson,et al.  More than the sum of its members: challenges for group recommender systems , 2004, AVI.

[16]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[17]  Michael J. Pazzani,et al.  Learning and Revising User Profiles: The Identification of Interesting Web Sites , 1997, Machine Learning.

[18]  Markus Stumptner,et al.  Consistency-based diagnosis of configuration knowledge bases , 1999, Artif. Intell..

[19]  Barry Smyth,et al.  On the Dynamic Generation of Compound Critiques in Conversational Recommender Systems , 2004, AH.

[20]  Ulrich Junker,et al.  QUICKXPLAIN: Preferred Explanations and Relaxations for Over-Constrained Problems , 2004, AAAI.

[21]  Judith Masthoff,et al.  Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers , 2004, User Modeling and User-Adapted Interaction.

[22]  T. Betsch,et al.  The routines of decision making , 2005 .

[23]  Barry Smyth,et al.  Trust in recommender systems , 2005, IUI.

[24]  Gerhard Friedrich,et al.  An Integrated Environment for the Development of Knowledge-Based Recommender Applications , 2006, Int. J. Electron. Commer..

[25]  Barry Smyth,et al.  Social Interaction in the CATS Group Recommender ? , 2006 .

[26]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[27]  Barry Smyth,et al.  Case-Based Recommendation , 2007, The Adaptive Web.

[28]  Lars Schmidt-Thieme,et al.  Guest Editors' Introduction: Recommender Systems , 2007, IEEE Intell. Syst..

[29]  Barry Smyth,et al.  Recommendation to Groups , 2007, The Adaptive Web.

[30]  Francesco Ricci,et al.  Acquiring and Revising Preferences in a Critique-Based Mobile Recommender System , 2007, IEEE Intelligent Systems.

[31]  Alexander Felfernig,et al.  Persuasion in Knowledge-Based Recommendation , 2008, PERSUASIVE.

[32]  Alexander Felfernig,et al.  Constraint-based recommender systems: technologies and research issues , 2008, ICEC.

[33]  Guy Shani,et al.  A Survey of Accuracy Evaluation Metrics of Recommendation Tasks , 2009, J. Mach. Learn. Res..

[34]  A. Felfernig,et al.  Plausible Repairs for Inconsistent Requirements , 2009, IJCAI.

[35]  Jennifer Golbeck,et al.  Computing with Social Trust , 2008, Human-Computer Interaction Series.

[36]  Wesley W. Chu,et al.  A Social Network-Based Recommender System (SNRS) , 2010, Data Mining for Social Network Data.

[37]  Francesco Ricci,et al.  Recommendation and decision technologies for requirements engineering , 2010, RSSE '10.

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

[39]  Alexander Felfernig,et al.  Consumer decision making in knowledge-based recommendation , 2009, Journal of Intelligent Information Systems.

[40]  Barry Smyth,et al.  Experience-Based Critiquing: Reusing Critiquing Experiences to Improve Conversational Recommendation , 2010, ICCBR.

[41]  Aditya G. Parameswaran,et al.  Information seeking , 2011, Commun. ACM.

[42]  Ludovico Boratto,et al.  State-of-the-Art in Group Recommendation and New Approaches for Automatic Identification of Groups , 2011, Information Retrieval and Mining in Distributed Environments.

[43]  Bart P. Knijnenburg,et al.  Each to his own: how different users call for different interaction methods in recommender systems , 2011, RecSys '11.

[44]  George Angelos Papadopoulos,et al.  A Context Aware Recommender System for Creativity Support Tools , 2011, J. Univers. Comput. Sci..

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

[46]  Judith Masthoff,et al.  Group Recommender Systems: Combining Individual Models , 2011, Recommender Systems Handbook.

[47]  Alexander Felfernig,et al.  Recommendation Technologies for Configurable Products , 2011, AI Mag..

[48]  Judith Masthoff,et al.  Designing and Evaluating Explanations for Recommender Systems , 2011, Recommender Systems Handbook.

[49]  John Riedl,et al.  Collaborative Filtering Recommender Systems , 2011, Found. Trends Hum. Comput. Interact..

[50]  Alexander Felfernig,et al.  An efficient diagnosis algorithm for inconsistent constraint sets , 2011, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[51]  W. Marsden.  I and J , 2012 .

[52]  Alexander Felfernig,et al.  Improving the performance of unit critiquing , 2012, UMAP.

[53]  Alexander Felfernig,et al.  Personalized Diagnosis for Over-Constrained Problems , 2013, IJCAI.

[54]  Alexander Felfernig,et al.  Toward the Next Generation of Recommender Systems: Applications and Research Challenges , 2013 .

[55]  Rudolf Grünig,et al.  Successful Decision-Making , 2013 .

[56]  Alexander Felfernig,et al.  ReComment: towards critiquing-based recommendation with speech interaction , 2013, RecSys.

[57]  Thomas L. Saaty,et al.  Criteria for Evaluating Group Decision-Making Methods , 2013 .

[58]  Sudeep Bhatia,et al.  Associations and the accumulation of preference. , 2013, Psychological review.

[59]  Mária Bieliková,et al.  Group Recommendations: Survey and Perspectives , 2014, Comput. Informatics.

[60]  Alexander Felfernig,et al.  Basic Approaches in Recommendation Systems , 2014, Recommendation Systems in Software Engineering.

[61]  Alexander Felfernig,et al.  INTELLIREQ: Intelligent Techniques for Software Requirements Engineering , 2014, ECAI.

[62]  Choicla : Intelligent Decision Support for Groups of Users in the Context of Personnel Decisions , 2014 .

[63]  Katharina Reinecke,et al.  Choice Architecture for Human-Computer Interaction , 2014, Found. Trends Hum. Comput. Interact..

[64]  Alexander Felfernig,et al.  Knowledge-Based Configuration: From Research to Business Cases , 2014 .

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

[66]  Alexander Felfernig,et al.  Counteracting Serial Position Effects in the CHOICLA Group Decision Support Environment , 2015, IUI.

[67]  Nava Tintarev,et al.  Human Interaction with Artificial Advice Givers , 2016 .

[68]  Dimitris Paraschakis.  Recommender Systems from an Industrial and Ethical Perspective , 2016, RecSys.

[69]  Andrea Janes,et al.  What recommendation systems for software engineering recommend: A systematic literature review , 2016, J. Syst. Softw..

[70]  Katrien Verbert,et al.  Recommender Systems for Health Informatics: State-of-the-Art and Future Perspectives , 2016, Machine Learning for Health Informatics.

[71]  Charu C. Aggarwal,et al.  Recommender Systems: The Textbook , 2016 .

[72]  Thi Ngoc Trang Tran,et al.  Towards Group-Based Configuration , 2016 .

[73]  Alexander Felfernig,et al.  Recommending and Configuring Smart Home Installations , 2016 .

[74]  Lorena Recalde,et al.  A Social Framework for Set Recommendation in Group Recommender Systems , 2016, ECIR.

[75]  Alexander Felfernig,et al.  An Analysis of Group Recommendation Heuristics for High- and Low-Involvement Items , 2017, IEA/AIE.

[76]  Alexander Brodsky,et al.  A Group Recommender for Investment in Microgrid Renewable Energy Sources , 2017, HICSS.

[77]  Greg Linden,et al.  Two Decades of Recommender Systems at Amazon.com , 2017, IEEE Internet Computing.

[78]  John Riedl,et al.  An Algorithmic Framework for Performing Collaborative Filtering , 1999, SIGIR Forum.

[79]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[80]  Tsuyoshi Murata,et al.  {m , 1934, ACML.