Recommender Systems Beyond E-Commerce: Presence and Future

Recommender systems are supporting users in the identification of items that fulfill their wishes and needs and are also helping to foster consumer happiness. These systems have been successfully applied in different application domains—examples thereof are the recommendation of movies, books, digital cameras, points of interest, financial services, and software requirements. The major objectives of this chapter are to provide an overview of recommendation approaches including criteria when to use which algorithm, to show different applications of recommendation algorithms going beyond standard e-commerce scenarios and to discuss issues for future research.

[1]  Alexander Felfernig,et al.  Towards Utility-Based Prioritization of Requirements in Open Source Environments , 2018, 2018 IEEE 26th International Requirements Engineering Conference (RE).

[2]  Alexander Felfernig,et al.  Towards Social Choice-based Explanations in Group Recommender Systems , 2019, UMAP.

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

[4]  Maryam Ramezani,et al.  Matching Recommendation Technologies and Domains , 2011, Recommender Systems Handbook.

[5]  Alexander Felfernig,et al.  Utility-Based Repair of Inconsistent Requirements , 2009, IEA/AIE.

[6]  Bamshad Mobasher,et al.  Recommender Systems as Multistakeholder Environments , 2017, UMAP.

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

[8]  Barry Smyth,et al.  Group recommender systems: a critiquing based approach , 2006, IUI '06.

[9]  Markus Zanker,et al.  Constraint-Based Recommendation for Software Project Effort Estimation , 2010 .

[10]  Alexander Felfernig,et al.  Group Decision Support for Requirements Negotiation , 2011, UMAP Workshops.

[11]  Leilani Battle,et al.  Building the Internet of Things Using RFID: The RFID Ecosystem Experience , 2009, IEEE Internet Computing.

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

[13]  Alexander Felfernig,et al.  Group Decision Support for Requirements Management Processes , 2018, ConfWS.

[14]  Daniel Sabin,et al.  Product Configuration Frameworks - A Survey , 1998, IEEE Intell. Syst..

[15]  Ronald Chung,et al.  Integrated personal recommender systems , 2007, ICEC.

[16]  Martin Ester,et al.  CrimeWalker: a recommendation model for suspect investigation , 2011, RecSys '11.

[17]  Alexander Felfernig,et al.  New Approaches to the Identification of Dependencies between Requirements , 2019, 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI).

[18]  Jane Cleland-Huang,et al.  Recommender Systems in Requirements Engineering , 2011, AI Mag..

[19]  Jon Oberlander,et al.  User preferences can drive facial expressions: evaluating an embodied conversational agent in a recommender dialogue system , 2010, User Modeling and User-Adapted Interaction.

[20]  Andrew E. Fano,et al.  Personal choice point: helping users visualize what it means to buy a BMW , 2003, IUI '03.

[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]  Shlomo Berkovsky,et al.  Recommender algorithms in activity motivating games , 2010, RecSys '10.

[23]  Mik Kersten,et al.  Using task context to improve programmer productivity , 2006, SIGSOFT '06/FSE-14.

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

[25]  Mel Ó Cinnéide,et al.  Rascal: A Recommender Agent for Agile Reuse , 2005, Artificial Intelligence Review.

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

[27]  Barry Smyth,et al.  Fast starters and slow finishers: A large-scale data analysis of pacing at the beginning and end of the marathon for recreational runners , 2018, Journal of Sports Analytics.

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

[29]  John Riedl,et al.  Techlens: a researcher's desktop , 2007, RecSys '07.

[30]  Harri Oinas-Kukkonen,et al.  Influencing Individually: Fusing Personalization and Persuasion , 2012, TIIS.

[31]  Young Park,et al.  A time-based approach to effective recommender systems using implicit feedback , 2008, Expert Syst. Appl..

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

[33]  Dirk Thorleuchter,et al.  Mining ideas from textual information , 2010, Expert Syst. Appl..

[34]  Egon L. van den Broek,et al.  Tune in to your emotions: a robust personalized affective music player , 2012, User Modeling and User-Adapted Interaction.

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

[36]  Robin D. Burke,et al.  Educational Recommendation with Multiple Stakeholders , 2016, 2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops (WIW).

[37]  Loriene Roy,et al.  Content-based book recommending using learning for text categorization , 1999, DL '00.

[38]  Shawn P. Curley,et al.  Recommender systems, consumer preferences, and anchoring effects , 2011, RecSys 2011.

[39]  Alexander Felfernig,et al.  Counteracting Anchoring Effects in Group Decision Making , 2015, UMAP.

[40]  Xavier Franch,et al.  Needs and challenges for a platform to support large-scale requirements engineering: a multiple-case study , 2018, ESEM.

[41]  Alexander Felfernig,et al.  Personalized diagnoses for inconsistent user requirements , 2011, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[42]  David C. Wilson,et al.  SmartChoice: An Online Recommender System to Support Low-Income Families in Public School Choice , 2009, AI Mag..

[43]  Alexander Felfernig,et al.  An overview of recommender systems in the healthy food domain , 2017, Journal of Intelligent Information Systems.

[44]  Paulo J. G. Lisboa,et al.  The value of personalised recommender systems to e-business: a case study , 2008, RecSys '08.

[45]  Ayse Basar Bener,et al.  AI-Based Software Defect Predictors : Applications and Benefits in a Case Study , 2011 .

[46]  S. Durmusoglu Open Innovation: The New Imperative for Creating and Profiting from Technology , 2004 .

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

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

[49]  Alexander Felfernig,et al.  An Overview of Recommender Systems in Requirements Engineering , 2013, Managing Requirements Knowledge.

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

[51]  Yehuda Koren,et al.  Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition , 2008, KDD 2008.

[52]  Neoklis Polyzotis,et al.  Query Recommendations for Interactive Database Exploration , 2009, SSDBM.

[53]  Franz Lehner,et al.  Requirements Engineering as a Success Factor in Software Projects , 2001, IEEE Softw..

[54]  Tiffany Ya Tang,et al.  The role of user mood in movie recommendations , 2010, Expert Syst. Appl..

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

[56]  Alexander Felfernig,et al.  KNOWLEDGECHECKR: Intelligent Techniques for Counteracting Forgetting , 2021, ECAI.

[57]  Alexander Felfernig,et al.  Minimization of decoy effects in recommender result sets , 2012, Web Intell. Agent Syst..

[58]  Walid Maalej,et al.  Potentials and challenges of recommendation systems for software development , 2008, RSSE '08.

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

[60]  Li Chen,et al.  Human Decision Making and Recommender Systems , 2015, Recommender Systems Handbook.

[61]  Carlos Delgado Kloos,et al.  A Collaborative Recommender System Based on Space-Time Similarities , 2010, IEEE Pervasive Computing.

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

[63]  Dietmar Jannach,et al.  SAT: A Web-Based Interactive Advisor for Investor-Ready Business Plans , 2007, ICE-B.

[64]  Justin Donaldson,et al.  The Big Promise of Recommender Systems , 2011, AI Mag..

[65]  Carl A. Gunter,et al.  Collaborative recommender systems for building automation , 2009 .

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

[67]  Alexander Felfernig,et al.  Towards Persuasive Technology for Software Development Environments: An Empirical Study , 2012, PERSUASIVE.

[68]  Alexander Felfernig,et al.  Status quo bias in configuration systems , 2011, IEA/AIE'11.

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

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

[71]  Ludovico Boratto Group Recommender Systems , 2016, RecSys.

[72]  A. Felfernig,et al.  Künstliche Intelligenz in der Öffentlichen Verwaltung , 2019, Handbuch E-Government.

[73]  Brad A. Myers,et al.  The Design and Evaluation of User Interfaces for the RADAR Learning Personal Assistant , 2009, AI Mag..

[74]  Loren Terveen,et al.  Beyond Recommender Systems: Helping People Help Each Other , 2001 .

[75]  Janice Singer,et al.  Hipikat: a project memory for software development , 2005, IEEE Transactions on Software Engineering.

[76]  Lloyd Blanchard,et al.  in Public Administration , 2016 .

[77]  Nicholas Jing Yuan,et al.  T-Finder: A Recommender System for Finding Passengers and Vacant Taxis , 2013, IEEE Transactions on Knowledge and Data Engineering.

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

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

[80]  Yanghua Xiao,et al.  Non-Compensatory Psychological Models for Recommender Systems , 2019, AAAI.

[81]  Alexander Felfernig,et al.  An overview of recommender systems in the internet of things , 2018, Journal of Intelligent Information Systems.

[82]  Robert J. Walker,et al.  Approximate Structural Context Matching: An Approach to Recommend Relevant Examples , 2006, IEEE Transactions on Software Engineering.

[83]  Alexander Felfernig,et al.  Beyond Item Recommendation: Using Recommendations to Stimulate Knowledge Sharing in Group Decisions , 2017, SocInfo.

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

[85]  Cihan Kaleli,et al.  A review on deep learning for recommender systems: challenges and remedies , 2018, Artificial Intelligence Review.

[86]  Georg Groh,et al.  Team recommendation in open innovation networks , 2009, RecSys '09.

[87]  Alexander Felfernig,et al.  Matrix factorization based heuristics for constraint-based recommenders , 2019, SAC.

[88]  Hao Jiang,et al.  Personalized online document, image and video recommendation via commodity eye-tracking , 2008, RecSys '08.

[89]  Alexander Felfernig,et al.  Reducing the Entry Threshold of AAL Systems: Preliminary Results from Casa Vecchia , 2012, ICCHP.

[90]  Youri van Pinxteren,et al.  Deriving a recipe similarity measure for recommending healthful meals , 2011, IUI '11.

[91]  Farshad Fotouhi,et al.  TupleRecommender: A Recommender System for Relational Databases , 2011, 2011 22nd International Workshop on Database and Expert Systems Applications.

[92]  Virginia E. Barker,et al.  Expert systems for configuration at Digital: XCON and beyond , 1989, Commun. ACM.

[93]  Tovi Grossman,et al.  Deploying CommunityCommands: A Software Command Recommender System Case Study , 2014, AI Mag..

[94]  Gerhard Friedrich,et al.  Recommender Systems: RECENT DEVELOPMENTS , 2010 .

[95]  Yo-Ping Huang,et al.  Experiences with RFID-based interactive learning in museums , 2010, Int. J. Auton. Adapt. Commun. Syst..

[96]  Charles J. Petrie,et al.  Semantic Email Addressing: The Semantic Web Killer App? , 2009, IEEE Internet Computing.

[97]  Alexander Felfernig,et al.  Empirical Knowledge Engineering: Cognitive Aspects in the Development of Constraint-Based Recommenders , 2010, IEA/AIE.

[98]  Walid Maalej,et al.  Requirements Intelligence with OpenReq Analytics , 2019, 2019 IEEE 27th International Requirements Engineering Conference (RE).

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

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

[101]  Juan Carlos Augusto,et al.  Ambient Intelligence—the Next Step for Artificial Intelligence , 2008, IEEE Intelligent Systems.

[102]  Michael Roberts,et al.  Collaborative Filtering Is Not Enough? Experiments with a Mixed-Model Recommender for Leisure Activities , 2009, UMAP.

[103]  Nitesh V. Chawla,et al.  Reliable medical recommendation systems with patient privacy , 2013, ACM Trans. Intell. Syst. Technol..

[104]  Xiongcai Cai,et al.  A Deployed People-to-People Recommender System in Online Dating , 2015, AI Mag..

[105]  Alexander Felfernig,et al.  Learned Constraint Ordering for Consistency Based Direct Diagnosis , 2019, IEA/AIE.

[106]  Elisabeth André,et al.  MED-StyleR: METABO diabetes-lifestyle recommender , 2010, RecSys '10.