Michael Jugovac
Dietmar Jannach
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Abstract
Automated recommendations have become a ubiquitous part of today’s online user experience. These systems point us to additional items to purchase in online shops, they make suggestions to us on movies to watch, or recommend us people to connect with on social websites. In many of today’s applications, however, the only way for users to interact with the system is to inspect the recommended items. Often, no mechanisms are implemented for users to give the system feedback on the recommendations or to explicitly specify preferences, which can limit the potential overall value of the system for its users.
Academic research in recommender systems is largely focused on algorithmic approaches for item selection and ranking. Nonetheless, over the years a variety of proposals were made on how to design more interactive recommenders. This work provides a comprehensive overview on the existing literature on user interaction aspects in recommender systems. We cover existing approaches for preference elicitation and result presentation, as well as proposals that consider recommendation as an interactive process. Throughout the work, we furthermore discuss examples of real-world systems and outline possible directions for future works.
- Gediminas Adomavicius, Jesse C. Bockstedt, Shawn Curley, and Jingjing Zhang. 2012. Effects of online recommendations on consumers’ willingness to pay. In Proceedings of the 2nd Workshop on Human Decision Making in Recommender Systems (Decisions@RecSys’12). 40--45.Google Scholar
- Gediminas Adomavicius, Jesse C. Bockstedt, Shawn Curley, and Jingjing Zhang. 2013. Do recommender systems manipulate consumer preferences? A study of anchoring effects. Information Systems Research 24, 4 (2013), 956--975. Google ScholarDigital Library
- Gediminas Adomavicius and YoungOk Kwon. 2007. New recommendation techniques for multicriteria rating systems. Intelligent Systems 22, 3 (2007), 48--55.Google ScholarDigital Library
- Gediminas Adomavicius and Alexander Tuzhilin. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. Transactions on Knowledge and Data Engineering 17, 6 (2005), 734--749. Google ScholarDigital Library
- Muhammad Aljukhadar, Sylvain Senecal, and Charles-Etienne Daoust. 2012. Using recommendation agents to cope with information overload. International Journal of Electronic Commerce 17, 2 (2012), 41--70. Google ScholarCross Ref
- Xavier Amatriain, Josep M. Pujol, and Nuria Oliver. 2009. I like it... I like it not: Evaluating user ratings noise in recommender systems. In Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization (UMAP’09). 247--258.Google ScholarDigital Library
- Xavier Amatriain, Josep M. Pujol, Nava Tintarev, and Nuria Oliver. 2009. Rate it again: Increasing recommendation accuracy by user re-rating. In Proceedings of the 3rd Conference on Recommender Systems (RecSys’09). 173--180.Google ScholarDigital Library
- Mohammad Shafkat Amin, Baoshi Yan, Sripad Sriram, Anmol Bhasin, and Christian Posse. 2012. Social referral: Leveraging network connections to deliver recommendations. In Proceedings of the 6th Conference on Recommender Systems (RecSys’12). 273--276.Google ScholarDigital Library
- Taiwo Amoo and Hershey H. Friedman. 2000. Overall evaluation rating scales: An assessment. International Journal of Market Research 42, 3 (2000), 301--310.Google ScholarCross Ref
- Taiwo Amoo and Hershey H. Friedman. 2001. Do numeric values influence subjects’ responses to rating scales? Journal of International Marketing and Marketing Research 26 (2001), 41--46.Google Scholar
- Sarabjot Singh Anand and Nathan Griffiths. 2011. A market-based approach to address the new item problem. In Proceedings of the 5th Conference on Recommender Systems (RecSys’11). 205--212. Google ScholarDigital Library
- Ivana Andjelkovic, Denis Parra, and John O’Donovan. 2016. Moodplay: Interactive mood-based music discovery and recommendation. In Proceedings of the 24th Conference on User Modeling, Adaptation, and Personalization (UMAP’16). 275--279.Google ScholarDigital Library
- Roland Bader, Wolfgang Wörndl, Andreas Karitnig, and Gerhard Leitner. 2011. Designing an explanation interface for proactive recommendations in automotive scenarios. In Proceedings of the Workshop on Advances in User Modeling at the 19th Conference on User Modeling, Adaptation, and Personalization (UMAP’11). 92--104.Google Scholar
- Sam Banks, Rachael Rafter, and Barry Smyth. 2015. The recommendation game: Using a game-with-a-purpose to generate recommendation data. In Proceedings of the 9th Conference on Recommender Systems (RecSys’15). 305--308.Google Scholar
- Patrick Baudisch and Lars Brueckner. 2002. TV scout: Lowering the entry barrier to personalized TV program recommendation. In Proceedings of the 2nd International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH’02). 58--68.Google ScholarCross Ref
- Claudia Becerra, Fabio Gonzalez, and Alexander Gelbukh. 2011. Visualizable and explicable recommendations obtained from price estimation functions. In Joint Proceedings of the RecSys 2011 Workshop on Human Decision Making in Recommender Systems (Decisions@RecSys’11) and User-Centric Evaluation of Recommender Systems and Their Interfaces-2 (UCERSTI 2) affiliated with the 5th Conference on Recommender Systems (RecSys’11). 27--34.Google Scholar
- Shay Ben-Elazar and Noam Koenigstein. 2014. A hybrid explanations framework for collaborative filtering recommender systems. In Poster Proceedings of the 8th Conference on Recommender Systems (RecSys’14).Google Scholar
- Nofar Dali Betzalel, Bracha Shapira, and Lior Rokach. 2015. “Please, not now!”: A model for timing recommendations. In Proceedings of the 9th Conference on Recommender Systems (RecSys’15). 297--300.Google Scholar
- Mustafa Bilgic and Raymond Mooney. 2005. Explaining recommendations: Satisfaction vs. promotion. In Proceedings of Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research at the 10th International Conference on Intelligent User Interfaces (IUI’05). 13--18.Google Scholar
- Dirk G. F. M. Bollen, Mark P. Graus, and Martijn C. Willemsen. 2012. Remembering the stars?: Effect of time on preference retrieval from memory. In Proceedings of the 6th Conference on Recommender Systems (RecSys’12). 217--220.Google Scholar
- Dirk G. F. M. Bollen, Bart P. Knijnenburg, Martijn C. Willemsen, and Mark P. Graus. 2010. Understanding choice overload in recommender systems. In Proceedings of the 4th Conference on Recommender Systems (RecSys’10). 63--70. Google ScholarDigital Library
- Svetlin Bostandjiev, John O’Donovan, and Tobias Höllerer. 2012. TasteWeights: A visual interactive hybrid recommender system. In Proceedings of the 4th Conference on Recommender Systems (RecSys’10). 35--42.Google ScholarDigital Library
- Derek G. Bridge. 2002. Towards conversational recommender systems: A dialogue grammar approach. In Proceedings of the 6th European Conference on Case Based Reasoning (ECCBR’02). 9--22.Google Scholar
- Robin Burke. 2000. Knowledge-based recommender systems. Encyclopedia of Library and Information Science 69, 32 (2000), 180--200.Google Scholar
- Robin D. Burke, Kristian J. Hammond, and Benjamin C. Young. 1996. Knowledge-based navigation of complex information spaces. In Proceedings of the 13th National Conference on Artificial Intelligence (AAAI’96). 462--468.Google ScholarDigital Library
- Duen Horng Chau, Aniket Kittur, Jason I. Hong, and Christos Faloutsos. 2011. Apolo: Interactive large graph sensemaking by combining machine learning and visualization. In Proceedings of the 17th SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’11). 739--742.Google ScholarDigital Library
- Li Chen. 2009. Adaptive tradeoff explanations in conversational recommenders. In Proceedings of the 3rd Conference on Recommender Systems (RecSys’09). 225--228. Google ScholarDigital Library
- Li Chen and Pearl Pu. 2006. Evaluating critiquing-based recommender agents. In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI’06). 157--162.Google Scholar
- Li Chen and Pearl Pu. 2008. A cross-cultural user evaluation of product recommender interfaces. In Proceedings of the 2nd Conference on Recommender Systems (RecSys’08). 75--82. Google ScholarDigital Library
- Li Chen and Ho Keung Tsoi. 2011. Users’ decision behavior in recommender interfaces: Impact of layout design. In Joint Proceedings of the RecSys 2011 Workshop on Human Decision Making in Recommender Systems (Decisions@RecSys’11) and User-Centric Evaluation of Recommender Systems and Their Interfaces-2 (UCERSTI 2) affiliated with the 5th Conference on Recommender Systems (RecSys’11). 21--26.Google Scholar
- Yu Chen and Pearl Pu. 2012. CoFeel: An interface for providing emotional feedback in mobile group recommender systems. In Joint Proceedings of the 1st International Workshop on Recommendation Technologies for Lifesytle Change (LIFESTYLE’12) and the 1st International Workshop on Interfaces for Recommender Systems (InterfaceRS’12). 48--55.Google Scholar
- Dan Cosley, Shyong K. Lam, Istvan Albert, Joseph A. Konstan, and John Riedl. 2003. Is seeing believing?: How recommender system interfaces affect users’ opinions. In Proceedings of the 2003 SIGCHI Conference on Human Factors in Computing Systems (CHI’03). 585--592.Google ScholarDigital Library
- Paolo Cremonesi, Antonio Donatacci, Franca Garzotto, and Roberto Turrin. 2012. Decision-making in recommender systems: The role of user’s goals and bounded resources. In Proceedings of the 2nd Workshop on Human Decision Making in Recommender Systems (Decisions@RecSys’12). 1--7.Google Scholar
- Paolo Cremonesi, Franca Garzotto, and Roberto Turrin. 2012. Investigating the persuasion potential of recommender systems from a quality perspective: An empirical study. Transactions on Interactive Intelligent Systems 2, 2 (2012), 11:1--11:41.Google Scholar
- Paolo Cremonesi, Franca Garzottto, and Roberto Turrin. 2012. User effort vs. accuracy in rating-based elicitation. In Proceedings of the 6th Conference on Recommender Systems (RecSys’12). 27--34.Google ScholarDigital Library
- Elizabeth M. Daly, Adi Botea, Akihiro Kishimoto, and Radu Marinescu. 2014. Multi-criteria journey aware housing recommender system. In Proceedings of the 8th Conference on Recommender Systems (RecSys’14). 325--328. Google ScholarDigital Library
- Toon De Pessemier, Simon Dooms, and Luc Martens. 2012. Design and evaluation of a group recommender system. In Proceedings of the 6th Conference on Recommender Systems (RecSys’12). 225--228. Google ScholarDigital Library
- Lihi Naamani Dery, Meir Kalech, Lior Rokach, and Bracha Shapira. 2014. Preference elicitation for narrowing the recommended list for groups. In Proceedings of the 8th Conference on Recommender Systems (RecSys’14). 333--336.Google Scholar
- Ravi Dhar and Itamar Simonson. 2003. The effect of forced choice on choice. Journal of Marketing Research 40, 2 (2003), 146--160. Google ScholarCross Ref
- Cecilia di Sciascio, Vedran Sabol, and Eduardo E. Veas. 2015. uRank: Exploring document recommendations through an interactive user-driven approach. In Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’15) co-located with the 9th Conference on Recommender Systems (RecSys’15). 29--36.Google Scholar
- Kristin Diehl and Cait Poynor. 2010. Great expectations?! Assortment size, expectations, and satisfaction. Journal of Marketing Research 47, 2 (2010), 312--322. arXiv:http://dx.doi.org/10.1509/jmkr.47.2.312 Google ScholarCross Ref
- Kristin Diehl, Erica van Herpen, and Cait Lamberton. 2015. Organizing products with complements versus substitutes: Effects on store preferences as a function of effort and assortment perceptions. Journal of Retailing 91, 1 (2015), 1--18. Google ScholarCross Ref
- Tim Donkers, Benedikt Loepp, and Jürgen Ziegler. 2015. Merging latent factors and tags to increase interactive control of recommendations. In Poster Proceedings of the 9th Conference on Recommender Systems (RecSys’15).Google Scholar
- Simon Dooms, Toon De Pessemier, and Luc Martens. 2014. Improving IMDb movie recommendations with interactive settings and filters. In Poster Proceedings of the 8th Conference on Recommender Systems (RecSys’14).Google Scholar
- Sara Drenner, Shilad Sen, and Loren G. Terveen. 2008. Crafting the initial user experience to achieve community goals. In Proceedings of the 2nd Conference on Recommender Systems (RecSys’08). 187--194. Google ScholarDigital Library
- Greg Dunn, Jurgen Wiersema, Jaap Ham, and Lora Aroyo. 2009. Evaluating interface variants on personality acquisition for recommender systems. In Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization (UMAP’09). 259--270. Google ScholarDigital Library
- Michael D. Ekstrand, Daniel Kluver, F. Maxwell Harper, and Joseph A. Konstan. 2015. Letting users choose recommender algorithms: An experimental study. In Proceedings of the 9th Conference on Recommender Systems (RecSys’15). 11--18.Google Scholar
- Mehdi Elahi, Matthias Braunhofer, Francesco Ricci, and Marko Tkalcic. 2013. Personality-based active learning for collaborative filtering recommender systems. In Proceedings of the 25th International Conference of the Italian Association for Artificial Intelligence (AI*IA’13). 360--371. Google ScholarDigital Library
- Mehdi Elahi, Mouzhi Ge, Francesco Ricci, Ignacio Fernández-Tobías, Shlomo Berkovski, and David Massimo. 2015. Interaction design in a mobile food recommender system. In Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’15) Co-located with the 9th Conference on Recommender Systems (RecSys’15). 49--52.Google Scholar
- Mehdi Elahi, Francesco Ricci, and Neil Rubens. 2013. Active learning strategies for rating elicitation in collaborative filtering: A system-wide perspective. Transactions on Intelligent Systems and Technology 5, 1 (2013), 13:1--13:33.Google Scholar
- Barbara Fasolo, Ralph Hertwig, Michaela Huber, and Mark Ludwig. 2009. Size, entropy, and density: What is the difference that makes the difference between small and large real-world assortments? Psychology 8 Marketing 26, 3 (2009), 254--279.Google Scholar
- Alexander Felfernig, Gerhard Friedrich, Klaus Isak, Kostyantyn Shchekotykhin, Erich Teppan, and Dietmar Jannach. 2009. Automated debugging of recommender user interface descriptions. Applied Intelligence 31, 1 (2009), 1--14. Google ScholarDigital Library
- Alexander Felfernig, Gerhard Friedrich, Dietmar Jannach, and Markus Zanker. 2007. An integrated environment for the development of knowledge-based recommender applications. International Journal of Electronic Commerce 11, 2 (2007), 11--34. Google ScholarDigital Library
- Alexander Felfernig, Gerhard Friedrich, Dietmar Jannach, and Markus Zanker. 2011. Developing constraint-based recommenders. In Recommender Systems Handbook (1st ed.), Francesco Ricci et al. (Ed.). Springer, 187--215. Springer. Google ScholarCross Ref
- Alexander Felfernig and Bartosz Gula. 2006. An empirical study on consumer behavior in the interaction with knowledge-based recommender applications. In Proceedings of the 8th International Conference on E-Commerce Technology (CEC’06)/3rd International Conference on Enterprise Computing, E-Commerce and E-Services (EEE’06). 37. Google ScholarDigital Library
- Alexander Felfernig, Bartosz Gula, Gerhard Leitner, Marco Maier, Rudolf Melcher, and Erich Teppan. 2008. Persuasion in knowledge-based recommendation. In Persuasive Technology. Lecture Notes in Computer Science, Vol. 5033. Springer, 71--82. Google ScholarDigital Library
- Gavan J. Fitzsimons and Donald R. Lehmann. 2004. Reactance to recommendations: When unsolicited advice yields contrary responses. Marketing Science 23, 1 (2004), 82--94. Google ScholarDigital Library
- Daniel Fleder and Kartik Hosanagar. 2009. Blockbuster culture’s next rise or fall: The impact of recommender systems on sales diversity. Management Science 55, 5 (2009), 697--712. Google ScholarDigital Library
- Brian J. Fogg. 1998. Persuasive computers: Perspectives and research directions. In Proceedings of the 1998 SIGCHI Conference on Human Factors in Computing Systems (CHI’98). 225--232.Google ScholarDigital Library
- Hershey H. Friedman and Taiwo Amoo. 1999. Rating the rating scales. Journal of Marketing Management 9, 3 (1999), 114--123.Google Scholar
- Matthias Fuchs and Markus Zanker. 2012. Multi-criteria ratings for recommender systems: An empirical analysis in the tourism domain. In Proceedings of the 13th International Conference on E-Commerce and Web Technologies (EC-Web’12). 100--111.Google ScholarCross Ref
- Gayatree Ganu, Noemie Elhadad, and Amélie Marian. 2009. Beyond the stars: Improving rating predictions using review text content. In Proceedings of the 12th International Workshop on the Web and Databases (WebDB’09).Google Scholar
- Rui Gao, Bibo Hao, Shuotian Bai, Lin Li, Ang Li, and Tingshao Zhu. 2013. Improving user profile with personality traits predicted from social media content. In Proceedings of the 7th Conference on Recommender Systems (RecSys’13). 355--358. Google ScholarDigital Library
- Damianos Gavalas, Charalampos Konstantopoulos, Konstantinos Mastakas, and Grammati Pantziou. 2014. Mobile recommender systems in tourism. Journal of Network and Computer Applications 39 (2014), 319--333. Google ScholarDigital Library
- Mouzhi Ge, Dietmar Jannach, Fatih Gedikli, and Martin Hepp. 2012. Effects of the placement of diverse items in recommendation lists. In Proceedings of the 14th International Conference on Enterprise Information Systems (ICEIS’12). 201--208.Google Scholar
- Fatih Gedikli and Dietmar Jannach. 2013. Improving recommendation accuracy based on item-specific tag preferences. Transactions on Intelligent Systems and Technology 4, 1 (2013), 11:1--11:19.Google Scholar
- Fatih Gedikli, Dietmar Jannach, and Mouzhi Ge. 2014. How should I explain? A comparison of different explanation types for recommender systems. International Journal of Human-Computer Studies 72, 4 (2014), 367--382. Google ScholarDigital Library
- Sofia Gkika and George Lekakos. 2014. The persuasive role of explanations in recommender systems. In Proceedings of the 2nd International Workshop on Behavior Change Support Systems (BCSS’14). 59--68.Google Scholar
- Mehmet Göker and Cynthia Thompson. 2000. The adaptive place advisor: A conversational recommendation system. In Proceedings of the 8th German Workshop on Case Based Reasoning. 187--198.Google Scholar
- Ken Goldberg, Theresa Roeder, Dhruv Gupta, and Chris Perkins. 2001. Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval 4, 2 (2001), 133--151. Google ScholarDigital Library
- Carlos A. Gomez-Uribe and Neil Hunt. 2015. The Netflix recommender system: Algorithms, business value, and innovation. Transactions on Management Information Systems 6, 4 (2015), 13:1--13:19.Google Scholar
- Eduardo Graells-Garrido, Mounia Lalmas, and Ricardo Baeza-Yates. 2016. Data portraits and intermediary topics: Encouraging exploration of politically diverse profiles. In Proceedings of the 21st International Conference on Intelligent User Interfaces (IUI’16). 228--240.Google ScholarDigital Library
- Peter Grasch, Alexander Felfernig, and Florian Reinfrank. 2013. ReComment: Towards critiquing-based recommendation with speech interaction. In Proceedings of the 7th Conference on Recommender Systems (RecSys’13). 157--164.Google ScholarDigital Library
- Shirley Gregor and Izak Benbasat. 1999. Explanations from intelligent systems: Theoretical foundations and implications for practice. MIS Quarterly 23, 4 (1999), 497--530. Google ScholarDigital Library
- Brynjar Gretarsson, John O’Donovan, Svetlin Bostandjiev, Christopher Hall, and Tobias Höllerer. 2010. SmallWorlds: Visualizing social recommendations. Computer Graphics Forum 29, 3 (2010), 833--842. Google ScholarDigital Library
- Ulrike Gretzel and Daniel R. Fesenmaier. 2006. Persuasion in recommender systems. International Journal of Electronic Commerce 11, 2 (2006), 81--100. Google ScholarDigital Library
- Francesca Guzzi, Francesco Ricci, and Robin D. Burke. 2011. Interactive multi-party critiquing for group recommendation. In Proceedings of the 5th Conference on Recommender Systems (RecSys’11). 265--268. Google ScholarDigital Library
- F. Maxwell Harper, Funing Xu, Harmanpreet Kaur, Kyle Condiff, Shuo Chang, and Loren G. Terveen. 2015. Putting users in control of their recommendations. In Proceedings of the 9th Conference on Recommender Systems (RecSys’15). 3--10. Google ScholarDigital Library
- Chen He, Denis Parra, and Katrien Verbert. 2016. Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities. Expert Systems with Applications 56 (2016), 9--27. Google ScholarDigital Library
- Jonathan L. Herlocker, Joseph A. Konstan, and John Riedl. 2000. Explaining collaborative filtering recommendations. In Proceedings of the 2000 Conference on Computer Supported Cooperative Work (CSCW’00). 241--250. Google ScholarDigital Library
- Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, and John T. Riedl. 2004. Evaluating collaborative filtering recommender systems. Transactions on Information Systems 22, 1 (2004), 5--53. Google ScholarDigital Library
- Yoshinori Hijikata, Yuki Kai, and Shogo Nishida. 2012. The relation between user intervention and user satisfaction for information recommendation. In Proceedings of the 27th Annual Symposium on Applied Computing (SAC’12). 2002--2007. Google ScholarDigital Library
- Daniel E. Ho and Kevin M. Quinn. 2008. Improving the presentation and interpretation of online ratings data with model-based figures. The American Statistician 62, 4 (2008), 279--288. Google ScholarCross Ref
- Wolfram Höpken, Matthias Fuchs, Markus Zanker, and Thomas Beer. 2010. Context-based adaptation of mobile applications in tourism. Information Technology 8 Tourism 12, 2 (2010), 175--195.Google Scholar
- Rong Hu and Pearl Pu. 2010. A study on user perception of personality-based recommender systems. In Proceedings of the 18th International Conference on User Modeling, Adaptation, and Personalization (UMAP’10). 291--302. Google ScholarDigital Library
- Jon Espen Ingvaldsen, Jon Atle Gulla, and Özlem Özgöbek. 2015. User controlled news recommendations. In Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’15) Co-located with the 9th Conference on Recommender Systems (RecSys’15). 45--48.Google Scholar
- Sheena S. Iyengar and Mark R. Lepper. 2000. When choice is demotivating: Can one desire too much of a good thing?Journal of Personality and Social Psychology 79, 6 (2000), 995--1006. Google ScholarCross Ref
- Niklas Jakob, Stefan Hagen Weber, Mark-Christoph Müller, and Iryna Gurevych. 2009. Beyond the stars: Exploiting free-text user reviews to improve the accuracy of movie recommendations. In Proceedings of the 1st International Workshop on Topic-Sentiment Analysis for Mass Opinion at the 2009 Conference on Information and Knowledge Management (CIKM’09). 57--64.Google ScholarDigital Library
- Anthony Jameson, Silvia Gabrielli, Per Ola Kristensson, Katharina Reinecke, Federica Cena, Cristina Gena, and Fabiana Vernero. 2011. How can we support users’ preferential choice?. In Extended Abstracts of the 2011 Conference on Human Factors in Computing Systems (CHI’11). 409--418.Google ScholarDigital Library
- Dietmar Jannach. 2004. Preference-based treatment of empty result sets in product finders and knowledge-based recommenders. In Proceedings of the 27th Annual Conference on Artificial Intelligence (AI’04). 145--159.Google Scholar
- Dietmar Jannach. 2006. Finding preferred query relaxations in content-based recommenders. In Proceedings of the 3rd International Conference on Intelligent Systems (IS’06). 355--360. Google ScholarCross Ref
- Dietmar Jannach and Gedas Adomavicius. 2016. Recommendations with a purpose. In Proceedings of the 10th Conference on Recommender Systems (RecSys’16). 7--10. Google ScholarDigital Library
- Dietmar Jannach and Gerold Kreutler. 2005. Personalized user preference elicitation for e-services. In Proceedings of the 2005 International Conference on e-Technology, e-Commerce, and e-Service (EEE’05). 604--611. Google ScholarDigital Library
- Dietmar Jannach and Gerold Kreutler. 2007. Rapid development of knowledge-based conversational recommender applications with advisor suite. Journal of Web Engineering 6, 2 (2007), 165--192.Google ScholarDigital Library
- Dietmar Jannach, Lukas Lerche, and Michael Jugovac. 2015. Item familiarity as a possible confounding factor in user-centric recommender systems evaluation. i-com Journal for Interactive Media 14, 1 (2015), 29--40.Google Scholar
- Dietmar Jannach, Lukas Lerche, and Markus Zanker. 2017. Social information access. In “Recommending based on Implicit Feedback” (forthcoming), Peter Brusilovsky and Daqing He (Eds.). Springer.Google Scholar
- Dietmar Jannach, Paul Resnick, Alexander Tuzhilin, and Markus Zanker. 2016. Recommender systems—Beyond matrix completion. Communications of the ACM 59, 11 (2016). Google ScholarDigital Library
- Dietmar Jannach, Markus Zanker, and Matthias Fuchs. 2014. Leveraging multi-criteria customer feedback for satisfaction analysis and improved recommendations. Information Technology 8 Tourism 14, 2 (2014), 119--149.Google Scholar
- Dietmar Jannach, Markus Zanker, Mouzhi Ge, and Marian Gröning. 2012. Recommender systems in computer science and information systems—A landscape of research. In Proceedings of the 13th International Conference on E-Commerce and Web Technologies (EC-WEB’12). 76--87.Google ScholarCross Ref
- Zhenhui Jiang, Weiquan Wang, and Izak Benbasat. 2005. Multimedia-based interactive advising technology for online consumer decision support. Communications of the ACM 48, 9 (2005), 92--98. Google ScholarDigital Library
- Rong Jin and Luo Si. 2004. A Bayesian approach toward active learning for collaborative filtering. In Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence (UAI’04). 278--285.Google ScholarDigital Library
- Thorsten Joachims, Laura Granka, Bing Pan, Helene Hembrooke, and Geri Gay. 2005. Accurately interpreting clickthrough data as implicit feedback. In Proceedings of the 28th Annual International SIGIR Conference on Research and Development in Information Retrieval (SIGIR’05). 154--161. Google ScholarDigital Library
- Hermann Kaindl, Elmar P. Wach, Ada Okoli, Roman Popp, Ralph Hoch, Werner Gaulke, and Tim Hussein. 2013. Semi-automatic generation of recommendation processes and their GUIs. In Proceedings of the 18th International Conference on Intelligent User Interfaces (IUI’13). 85--94. Google ScholarDigital Library
- Rasoul Karimi, Christoph Freudenthaler, Alexandros Nanopoulos, and Lars Schmidt-Thieme. 2012. Exploiting the characteristics of matrix factorization for active learning in recommender systems. In Proceedings of the 6th Conference on Recommender Systems (RecSys’12). 317--320. Google ScholarDigital Library
- Kristiina Karvonen, Sanna Shibasaki, Sofia Nunes, Puneet Kaur, and Olli Immonen. 2010. Visual nudges for enhancing the use and produce of reputation information. In Proceedings of the Workshop on User-Centric Evaluation of Recommender Systems and Their Interfaces at the 4th Conference on Recommender Systems (RecSys’10). 1--8.Google Scholar
- Diane Kelly and Nicholas J. Belkin. 2001. Reading time, scrolling and interaction: Exploring implicit sources of user preferences for relevance feedback. In Proceedings of the 24th Annual International SIGIR Conference on Research and Development in Information Retrieval (SIGIR’01). 408--409.Google Scholar
- Diane Kelly and Jaime Teevan. 2003. Implicit feedback for inferring user preference: A bibliography. SIGIR Forum 37, 2 (2003), 18--28.Google ScholarDigital Library
- Noreen M. Klein and Manjit S. Yadav. 1989. Context effects on effort and accuracy in choice: An enquiry into adaptive decision making. Journal of Consumer Research 15, 4 (1989), 411--421. Google ScholarCross Ref
- Daniel Kluver, Tien T. Nguyen, Michael D. Ekstrand, Shilad Sen, and John Riedl. 2012. How many bits per rating?. In Proceedings of the 6th Conference on Recommender Systems (RecSys’12). 99--106.Google ScholarDigital Library
- Bart P. Knijnenburg, Svetlin Bostandjiev, John O’Donovan, and Alfred Kobsa. 2012. Inspectability and control in social recommenders. In Proceedings of the 6th Conference on Recommender Systems (RecSys’12). 43--50.Google ScholarDigital Library
- Bart P. Knijnenburg, Niels J. M. Reijmer, and Martijn C. Willemsen. 2011. Each to his own: How different users call for different interaction methods in recommender systems. In Proceedings of the 5th Conference on Recommender Systems (RecSys’11). 141--148.Google Scholar
- Alfred Kobsa, Jürgen Koenemann, and Wolfgang Pohl. 2001. Personalised hypermedia presentation techniques for improving online customer relationships. The Knowledge Engineering Review 16, 2 (2001), 111--155.Google ScholarDigital Library
- Joseph Konstan and John Riedl. 2012. Recommender systems: From algorithms to user experience. User Modeling and User-Adapted Interaction 22, 1 (2012), 101--123. Google ScholarDigital Library
- Johannes Kunkel, Benedikt Loepp, and Jürgen Ziegler. 2015. 3D-visualisierung zur eingabe von Präferenzen in empfehlungssystemen. In Proceedings of the 2015 Conference on Mensch und Computer 2015. De Gruyter Oldenbourg, 123--132.Google ScholarCross Ref
- Anísio Lacerda, Adriano Veloso, and Nivio Ziviani. 2013. Exploratory and interactive daily deals recommendation. In Proceedings of the 7th Conference on Recommender Systems (RecSys’13). 439--442.Google ScholarDigital Library
- Joseph Lajos, Amitava Chattopadhyay, and Kishore Sengupta. 2009. When electronic recommendation agents backfire: Negative effects on choice satisfaction, attitudes, and purchase intentions. Advances in Consumer Research 36 (2009), 845--846.Google Scholar
- Béatrice Lamche, Ugur Adigüzel, and Wolfgang Wörndl. 2014. Interactive explanations in mobile shopping recommender systems. In Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’14) co-located with the 8th Conference on Recommender Systems (RecSys’14). 14--21.Google Scholar
- Béatrice Lamche, Enrico Pollok, Wolfgang Wörndl, and Georg Groh. 2014. Evaluating the effectiveness of stereotype user models for recommendations on mobile devices. In Proceedings of the 4th International Workshop on Personalization Approaches in Learning Environments (PALE’14), held in conjunction with the 22nd International Conference on User Modeling, Adaptation, and Personalization (UMAP’14). 33--41.Google Scholar
- Béatrice Lamche, Yannick Rödl, Claudius Hauptmann, and Wolfgang Wörndl. 2015. Context-aware recommendations for mobile shopping. In Proceedings of the Workshop on Location-Aware Recommendations (LocalRec’15) co-located with the 9th Conference on Recommender Systems (RecSys’15). 21--27.Google Scholar
- Danielle Hyunsook Lee. 2008. PITTCULT: Trust-based cultural event recommender. In Proceedings of the 2nd Conference on Recommender Systems (RecSys’08). 311--314.Google ScholarDigital Library
- Wei-Po Lee. 2004. Towards agent-based decision making in the electronic marketplace: Interactive recommendation and automated negotiation. Expert Systems with Applications 27, 4 (2004), 665--679. Google ScholarCross Ref
- Cane Wing-ki Leung, Stephen Chi-fai Chan, and Fu-lai Chung. 2006. Integrating collaborative filtering and sentiment analysis: A rating inference approach. In Proceedings of the 2006 Workshop on Recommender Systems in Conjunction with the 17th European Conference on Artificial Intelligence (ECAI’06). 62--66.Google Scholar
- Greg Linden, Steve Hanks, and Neal Lesh. 1997. Interactive assessment of user preference models: The automated travel assistant. In Proceedings of the 6th International Conference on User Modeling (UM’97), Vol. 383. 67--78.Google ScholarCross Ref
- Benedikt Loepp, Tim Hussein, and Jürgen Ziegler. 2014. Choice-based preference elicitation for collaborative filtering recommender systems. In Proceedings of the 2014 SIGCHI Conference on Human Factors in Computing Systems (CHI’14). 3085--3094.Google ScholarDigital Library
- Tariq Mahmood, Francesco Ricci, and Adriano Venturini. 2009. Improving recommendation effectiveness: Adapting a dialogue strategy in online travel planning. Information Technology 8 Tourism 11, 4 (2009), 285--302.Google Scholar
- Benjamin M. Marlin, Richard S. Zemel, Sam T. Roweis, and Malcolm Slaney. 2007. Collaborative filtering and the missing at random assumption. In Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence (UAI’07). 267--275.Google ScholarDigital Library
- Riccardo Mazza. 2009. Introduction to Information Visualization (1st ed.). Springer.Google Scholar
- Kevin McCarthy, James Reilly, Lorraine McGinty, and Barry Smyth. 2004. On the dynamic generation of compound critiques in conversational recommender systems. In Proceedings of the 3rd International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH’04). 176--184. Google ScholarCross Ref
- Kevin McCarthy, James Reilly, Lorraine McGinty, and Barry Smyth. 2004. Thinking positively—Explanatory feedback for conversational recommender systems. In Proceedings of the Explanation Workshop at the European Conference on Case-Based Reasoning (ECCBR’04). 115--124.Google Scholar
- Robert McCrae and Paul Costa. 1996. Toward a new generation of personality theories: Theoretical contexts for the five-factor model. In The Five-Factor Model of Personality: Theoretical Perspectives, Jerry S. Wiggins (Ed.). Guildford Press, 51--87. Guildford Press.Google Scholar
- Sean M. McNee, Shyong K. Lam, Joseph A. Konstan, and John Riedl. 2003. Interfaces for eliciting new user preferences in recommender systems. In Proceedings of the 9th International Conference on User Modeling (UM’03). 178--187. Google ScholarCross Ref
- Sean M. McNee, John Riedl, and Jospeh A. Konstan. 2006. Being accurate is not enough: How accuracy metrics have hurt recommender systems. In Extended Abstracts of the 2006 Conference on Human Factors in Computing Systems (CHI’06). 1097--1101.Google Scholar
- David McSherry. 2004. Incremental relaxation of unsuccessful queries. In Proceedings of the 7th European Conference on Case-Based Reasoning (ECCBR’04). 331--345. Google ScholarCross Ref
- Amine Naak, Hicham Hage, and Esma Ameur. 2009. A multi-criteria collaborative filtering approach for research paper recommendation in papyres. In E-Technologies: Innovation in an Open World. Lecture Notes in Business Information Processing, Vol. 26. Springer, 25--39. Google ScholarCross Ref
- Theodora Nanou, George Lekakos, and Konstantinos G. Fouskas. 2010. The effects of recommendations’ presentation on persuasion and satisfaction in a movie recommender system. Multimedia Systems 16, 4 (2010), 219--230. Google ScholarDigital Library
- Dario De Nart, Felice Ferrara, and Carlo Tasso. 2013. Personalized access to scientific publications: From recommendation to explanation. In Proceedings of the 21st International Conference on User Modeling, Adaptation, and Personalization (UMAP’13). 296--301.Google ScholarCross Ref
- Dario De Nart and Carlo Tasso. 2014. A personalized concept-driven recommender system for scientific libraries. In Proceedings of the 10th Italian Research Conference on Digital Libraries (IRCDL’14). 84--91. Google ScholarCross Ref
- Julia Neidhardt, Leonhard Seyfang, Rainer Schuster, and Hannes Werthner. 2015. A picture-based approach to recommender systems. Information Technology 8 Tourism 15, 1 (2015), 49--69.Google Scholar
- Quang Nhat Nguyen and Francesco Ricci. 2007. Replaying live-user interactions in the off-line evaluation of critique-based mobile recommendations. In Proceedings of the 7th Conference on Recommender Systems (RecSys’13). 81--88. Google ScholarDigital Library
- Tien T. Nguyen, Daniel Kluver, Ting-Yu Wang, Pik-Mai Hui, Michael D. Ekstrand, Martijn C. Willemsen, and John Riedl. 2013. Rating support interfaces to improve user experience and recommender accuracy. In Proceedings of the 7th Conference on Recommender Systems (RecSys’13). 149--156. Google ScholarDigital Library
- Stephen M. Nowlis, Barbara E. Kahn, and Ravi Dhar. 2002. Coping with ambivalence: The effect of removing a neutral option on consumer attitude and preference judgments. Journal of Consumer Research 29, 3 (2002), 319--334. Google ScholarCross Ref
- Maria Augusta S. N. Nunes and Rong Hu. 2012. Personality-based recommender systems: An overview. In Proceedings of the 6th Conference on Recommender Systems (Recsys’12). 5--6.Google Scholar
- Douglas W. Oard and Jinmook Kim. 1998. Implicit feedback for recommender systems. In Proceedings of the 1998 AAAI Workshop on Recommender Systems. 81--83.Google Scholar
- Mark O’Connor, Dan Cosley, Joseph A. Konstan, and John Riedl. 2001. PolyLens: A recommender system for groups of users. In Proceedings of the 7th Conference on European Conference on Computer Supported Cooperative Work (ECSCW’01). 199--218.Google ScholarDigital Library
- John O’Donovan, Barry Smyth, Brynjar Gretarsson, Svetlin Bostandjiev, and Tobias Höllerer. 2008. PeerChooser: Visual interactive recommendation. In Proceedings of the 2008 SIGCHI Conference on Human Factors in Computing Systems (CHI’08). 1085--1088.Google ScholarDigital Library
- Denis Parra and Peter Brusilovsky. 2015. User-controllable personalization: A case study with setfusion. International Journal of Human-Computer Studies 78 (2015), 43--67. Google ScholarDigital Library
- Denis Parra, Peter Brusilovsky, and Christoph Trattner. 2014. See what you want to see: Visual user-driven approach for hybrid recommendation. In Proceedings of the 19th International Conference on Intelligent User Interfaces (IUI’14). 235--240.Google ScholarDigital Library
- Evelien Perik, Boris de Ruyter, Panos Markopoulos, and Berry Eggen. 2004. The sensitivities of user profile information in music recommender systems. In Proceedings of the 2nd Annual Conference on Privacy, Security, and Trust (PST’04). 137--141.Google Scholar
- Carolin Plate, Nathalie Basselin, Alexander Kröner, Michael Schneider, Stephan Baldes, Vania Dimitrova, and Anthony Jameson. 2006. Recomindation: New functions for augmented memories. In Proceedings of the 4th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH’06). 141--150.Google ScholarDigital Library
- Alina Pommeranz, Joost Broekens, Pascal Wiggers, Willem-Paul Brinkman, and Catholijn M. Jonker. 2012. Designing interfaces for explicit preference elicitation: A user-centered investigation of preference representation and elicitation process. User Modeling and User-Adapted Interaction 22, 4 (2012), 357--397. Google ScholarDigital Library
- Verus Pronk, Wim F. J. Verhaegh, Adolf Proidl, and Marco Tiemann. 2007. Incorporating user control into recommender systems based on naive Bayesian classification. In Proceedings of the 1st Conference on Recommender Systems (RecSys’07). 73--80. Google ScholarDigital Library
- Pearl Pu and Li Chen. 2007. Trust-inspiring explanation interfaces for recommender systems. Knowledge-Based Systems 20, 6 (2007), 542--556. Google ScholarDigital Library
- Pearl Pu, Li Chen, and Rong Hu. 2011. A user-centric evaluation framework for recommender systems. In Proceedings of the 5th Conference on Recommender Systems (RecSys’11). 157--164. Google ScholarDigital Library
- Pearl Pu, Maoan Zhou, and Sylvain Castagnos. 2009. Critiquing recommenders for public taste products. In Proceedings of the 3rd Conference on Recommender Systems (RecSys’09). 249--252. Google ScholarDigital Library
- Al Mamunur Rashid, Istvan Albert, Dan Cosley, Shyong K. Lam, Sean M. McNee, Joseph A. Konstan, and John Riedl. 2002. Getting to know you: Learning new user preferences in recommender systems. In Proceedings of the 7th International Conference on Intelligent User Interfaces (IUI’02). 127--134.Google ScholarDigital Library
- Al Mamunur Rashid, George Karypis, and John Riedl. 2008. Learning preferences of new users in recommender systems: An information theoretic approach. SIGKDD Explorations Newsletter 10, 2 (2008), 90--100. Google ScholarDigital Library
- James Reilly, Kevin McCarthy, Lorraine McGinty, and Barry Smyth. 2004. Dynamic critiquing. In Advances in Case-Based Reasoning, Peter Funk et al. (Ed.). Springer, 763--777. Google ScholarCross Ref
- James Reilly, Kevin McCarthy, Lorraine McGinty, and Barry Smyth. 2005. Incremental critiquing. Knowledge-Based Systems 18, 4--5 (2005), 143--151.Google ScholarDigital Library
- Elena Reutskaja and Robin M. Hogarth. 2009. Satisfaction in choice as a function of the number of alternatives: When “goods satiate”. Psychology 8 Marketing 26, 3 (2009), 197--203.Google ScholarCross Ref
- Francesco Ricci. 2011. Mobile recommender systems. Information Technology and Tourism 12, 3 (2011), 205--231. Google ScholarCross Ref
- Francesco Ricci and Fabio Del Missier. 2004. Supporting travel decision making through personalized recommendation. In Designing Personalized User Experiences in eCommerce, Clare-Marie Karat et al. (Ed.). Human-Computer Interaction Series, Vol. 5. Springer, 231--251. Springer. Google ScholarCross Ref
- Francesco Ricci and Quang Nhat Nguyen. 2007. Acquiring and revising preferences in a critique-based mobile recommender system. Intelligent Systems 22, 3 (2007), 22--29. Google ScholarDigital Library
- Neil Rubens, Dain Kaplan, and Masashi Sugiyama. 2011. Active learning in recommender systems. In Recommender Systems Handbook (1st ed.), Francesco Ricci et al. (Ed.). Springer, 735--767. Google ScholarCross Ref
- Lloyd Rutledge, Natalia Stash, Yiwen Wang, and Lora Aroyo. 2008. Accuracy in rating and recommending item features. In Proceedings of the 5th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH’08). 163--172. Google ScholarDigital Library
- Thomas L. Saaty. 1977. A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology 15, 3 (1977), 234--281. Google ScholarCross Ref
- Thomas L. Saaty. 1988. What is the analytic hierarchy process? In Mathematical Models for Decision Support, Gautam Mitra et al. (Ed.). NATO ASI Series, Vol. 48. Springer, 109--121. Google ScholarCross Ref
- Adem Sabic and Markus Zanker. 2015. Investigating user’s information needs and attitudes towards proactivity in mobile tourist guides. In Information and Communication Technologies in Tourism 2015, Iis Tussyadiah et al. (Ed.). Springer, 493--505.Google Scholar
- Lara Quijano Sánchez, Juan A. Recio-García, and Belén Díaz-Agudo. 2011. Using personality to create alliances in group recommender systems. In Proceedings of the 19th International Conference on Case-Based Reasoning (ICCBR’11). 226--240.Google Scholar
- J. Ben Schafer, Joseph A. Konstan, and John Riedl. 2001. E-commerce recommendation applications. In Applications of Data Mining to Electronic Commerce, Ron Kohavi et al. (Ed.). Springer, 115--153. Google ScholarCross Ref
- J. Ben Schafer, Joseph A. Konstan, and John Riedl. 2002. Meta-recommendation systems: User-controlled integration of diverse recommendations. In Proceedings of the 11th International Conference on Information and Knowledge Management (CIKM’02). 43--51.Google ScholarDigital Library
- James Schaffer, Tobias Höllerer, and John O’Donovan. 2015. Hypothetical recommendation: A study of interactive profile manipulation behavior for recommender systems. In Proceedings of the 28th International Florida Artificial Intelligence Research Society Conference (FLAIRS’15). 507--512.Google Scholar
- Christian Schmitt, Dietmar Dengler, and Mathias Bauer. 2002. The MAUT-machine: An adaptive recommender system. In Proceedings of the 10th Workshop on Adaptivität und Benutzermodellierung in interaktiven Softwaresystemen (ABIS’02). 83--90.Google Scholar
- Michael Scholz and Verena Dorner. 2012. Estimating optimal recommendation set sizes for individual consumers. In Proceedings of the International Conference on Information Systems (ICIS’12). 2440--2459.Google Scholar
- Barry Schwartz. 2004. The Paradox of Choice: Why More is Less. Harper Perennial.Google Scholar
- Barry Schwartz, Andrew Ward, John Monterosso, Sonja Lyubomirsky, Katherine White, and Darrin R. Lehman. 2002. Maximizing versus satisficing: Happiness is a matter of choice.Journal of Personality and Social Psychology 83, 5 (2002), 1178.Google Scholar
- Aner Sela, Jonah Berger, and Wendy Liu. 2009. Variety, vice, and virtue: How assortment size influences option choice. Journal of Consumer Research 35, 6 (2009), 941--951. Google ScholarCross Ref
- Shilad Sen, Jesse Vig, and John Riedl. 2009. Tagommenders: Connecting users to items through tags. In Proceedings of the 18th International Conference on World Wide Web (WWW’09). 671--680.Google ScholarDigital Library
- Avni M. Shah and George Wolford. 2007. Buying behavior as a function of parametric variation of number of choices. Psychological Science 18, 5 (2007), 369--370. Google ScholarCross Ref
- Amit Sharma. 2013. PopCore: A system for network-centric recommendation. In Proceedings of the 2013 Conference on Computer Supported Cooperative Work (CSCW’13). 31--34. Google ScholarDigital Library
- Barry Smyth and Paul McClave. 2001. Similarity vs. diversity. In Case-Based Reasoning Research and Development. Lecture Notes in Computer Science, Vol. 2080. Springer, 347--361. Google ScholarCross Ref
- E. Isaac Sparling and Shilad Sen. 2011. Rating: How difficult is it?. In Proceedings of the 5th Conference on Recommender Systems (RecSys’11). 149--156.Google ScholarDigital Library
- Panagiotis Symeonidis, Alexandros Nanopoulos, and Yannis Manolopoulos. 2009. MoviExplain: A recommender system with explanations. In Proceedings of the 5th Conference on Recommender Systems (RecSys’11). 317--320. Google ScholarDigital Library
- Erich Teppan, Alexander Felfernig, and Klaus Isak. 2011. Decoy effects in financial service e-sales systems. In Joint Proceedings of the RecSys 2011 Workshop on Human Decision Making in Recommender Systems (Decisions@RecSys’11) and User-Centric Evaluation of Recommender Systems and Their Interfaces-2 (UCERSTI 2) affiliated with the 5th Conference on Recommender Systems (RecSys’11). 1--8.Google Scholar
- Nava Tintarev, Byungkyu Kang, Tobias Höllerer, and John O’Donovan. 2015. Inspection mechanisms for community-based content discovery in microblogs. In Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’15) co-located with the 9th Conference on Recommender Systems (RecSys’15). 21--28.Google Scholar
- Nava Tintarev and Judith Masthoff. 2007. A survey of explanations in recommender systems. In Proceedings of the 23rd International Conference on Data Engineering Workshops (ICDE’07). 801--810. Google ScholarDigital Library
- Nava Tintarev and Judith Masthoff. 2011. Designing and evaluating explanations for recommender systems. In Recommender Systems Handbook (1st ed.), Francesco Ricci et al. (Ed.). Springer, 479--510. Springer. Google ScholarCross Ref
- Amos Tversky and Daniel Kahneman. 1981. The framing of decisions and the psychology of choice. Science 211, 4481 (1981), 453--458. arXiv:http://science.sciencemag.org/content/211/4481/453.full.pdf.Google Scholar
- Katrien Verbert, Denis Parra, and Peter Brusilovsky. 2014. The effect of different set-based visualizations on user exploration of recommendations. In Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’14) co-located with the 8th Conference on Recommender Systems (RecSys’14). 37--44.Google Scholar
- Jesse Vig, Shilad Sen, and John Riedl. 2009. Tagsplanations: Explaining recommendations using tags. In Proceedings of the 14th International Conference on Intelligent User Interfaces (IUI’09). 47--56.Google Scholar
- Jesse Vig, Shilad Sen, and John Riedl. 2011. Navigating the tag genome. In Proceedings of the 16th International Conference on Intelligent User Interfaces (IUI’11). 93--102. Google ScholarDigital Library
- Michail Vlachos and Daniel Svonava. 2012. Graph embeddings for movie visualization and recommendation. In Joint Proceedings of the 1st International Workshop on Recommendation Technologies for Lifesytle Change (LIFESTYLE’12) and the 1st International Workshop on Interfaces for Recommender Systems (InterfaceRS’12). 56--59.Google Scholar
- Wesley Waldner and Julita Vassileva. 2014. Emphasize, don’t filter!: Displaying recommendations in Twitter timelines. In Proceedings of the 8th Conference on Recommender Systems (RecSys’14). 313--316.Google Scholar
- Martijn C. Willemsen, Bart P. Knijnenburg, Mark P. Graus, Linda C. M. Velter-Bremmers, and Kai Fu. 2011. Using latent features diversification to reduce choice difficulty in recommendation list. In Joint Proceedings of the RecSys 2011 Workshop on Human Decision Making in Recommender Systems (Decisions@RecSys’11) and User-Centric Evaluation of Recommender Systems and Their Interfaces-2 (UCERSTI 2) affiliated with the 5th Conference on Recommender Systems (RecSys’11). 14--20.Google Scholar
- Wolfgang Wörndl, Johannes Huebner, Roland Bader, and Daniel Gallego-Vico. 2011. A model for proactivity in mobile, context-aware recommender systems. In Proceedings of the 5th Conference on Recommender Systems (RecSys’11). 273--276.Google ScholarDigital Library
- Wolfgang Wörndl and Béatrice Lamche. 2015. User interaction with context-aware recommender systems on smartphones. i-com Journal for Interactive Media 14, 1 (2015), 19--28.Google Scholar
- Wolfgang Wörndl and Part Saelim. 2014. Voting operations for a group recommender system in a distributed user interface environment. In Poster Proceedings of the 8th Conference on Recommender Systems (RecSys’14).Google Scholar
- Wolfgang Wörndl, Jan Weicker, and Béatrice Lamche. 2013. Selecting gestural user interaction patterns for recommender applications on smartphones. In Proceedings of the 3rd Workshop on Human Decision Making in Recommender Systems (Decisions@Recsys’13). 17--20.Google Scholar
- Julie Wulff and Daniel Hardt. 2014. Can you trust online ratings? Evidence of systematic differences in user populations. In Proceedings of the 22nd European Conference on Information Systems (ECIS’14).Google Scholar
- Bo Xiao and Izak Benbasat. 2007. E-commerce product recommendation agents: Use, characteristics, and impact. MIS Quarterly 31, 1 (2007), 137--209.Google ScholarDigital Library
- Alexandre Yahi, Antoine Chassang, Louis Raynaud, Hugo Duthil, and Duen Horng (Polo) Chau. 2015. Aurigo: An interactive tour planner for personalized itineraries. In Proceedings of the 20th International Conference on Intelligent User Interfaces (IUI’15). 275--285.Google ScholarDigital Library
- Chun-Ya Yang and Soe-Tsyr Yuan. 2010. Color imagery for destination recommendation in regional tourism. In Proceedings of the 14th Pacific Asia Conference on Information Systems (PACIS’10).Google Scholar
- Kyung-Hyan Yoo, Ulrike Gretzel, and Markus Zanker. 2013. Persuasive Recommender Systems: Conceptual Background and Implications. Springer. Google ScholarCross Ref
- Markus Zanker and Daniel Ninaus. 2010. Knowledgeable explanations for recommender systems. In Proceedings of the International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT’10). 657--660. Google ScholarDigital Library
- Jiyong Zhang, Nicolas Jones, and Pearl Pu. 2008. A visual interface for critiquing-based recommender systems. In Proceedings of the 9th Conference on Electronic Commerce (EC’08). ACM, 230--239. Google ScholarDigital Library
- Jiyong Zhang and Pearl Pu. 2006. A comparative study of compound critique generation in conversational recommender systems. In Proceedings of the 4th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH’06). 234--243. Google ScholarDigital Library
- Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, and Shaoping Ma. 2014. Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In Proceedings of the 37th International Conference on Research and Development in Information Retrieval (SIGIR’14). 83--92. Google ScholarDigital Library
- Shiwan Zhao, Michelle X. Zhou, Quan Yuan, Xiatian Zhang, Wentao Zheng, and Rongyao Fu. 2010. Who is talking about what: Social map-based recommendation for content-centric social websites. In Proceedings of the 4th Conference on Recommender Systems (RecSys’10). 143--150.Google ScholarDigital Library
- Hua Zhou and Kenneth Lange. 2009. Rating movies and rating the raters who rate them. The American Statistician 63, 4 (2009), 297--307. Google ScholarCross Ref
- Chris Alvino and Justin Basilico. 2015. Learning a Personalized Homepage. Retrieved February, 2017 from http://techblog.netflix.com/2015/04/learning-personalized-homepage.html.Google Scholar
- Xavier Amatriain and Justin Basilico. 2012. Netflix Recommendations: Beyond the 5 stars (Part 1). Retrieved February, 2017 from http://techblog.netflix.com/2012/04/netflix-recommendations-beyond-5-stars.html.Google Scholar
- Nathan McAlone. 2016. Netflix wants to ditch its 5-star rating system. Retrieved February, 2017 from http://businessinsider.de/netflix-wants-to-ditch-5-star-ratings-2016-1.Google Scholar
- Merriam-Webster Dictionary. 2016. Persuasion. Retrieved February, 2017 from http://www.merriam-webster.com/dictionary/persuasion.Google Scholar
- Shiva Rajaraman. 2009. Five Stars Dominate Ratings. Retrieved February, 2017 from http://youtube-global.blogspot.de/2009/09/five-stars-dominate-ratings.html.Google Scholar
- Judy Robertson. 2011. Stats: We’re Doing It Wrong. Retrieved February, 2017 from http://cacm.acm.org/blogs/blog-cacm/107125-stats-were-doing-it-wrong.Google Scholar
- Harald Steck, Roelof van Zwol, and Chris Johnson. 2015. Interactive Recommender Systems with Netflix and Spotify. Retrieved February, 2017 from http://de.slideshare.net/MrChrisJohnson/interactive-recommender-systems-with-netflix-and-spotify.Google Scholar
- Kirsten Swearingen and Rashmi Sinha. 2002. Interaction design for recommender systems. Retrieved February, 2017 from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.15.73478rep=rep18type=pdf.Google Scholar
Index Terms
- Interacting with Recommenders—Overview and Research Directions
Recommendations
A 3D Item Space Visualization for Presenting and Manipulating User Preferences in Collaborative Filtering
IUI '17: Proceedings of the 22nd International Conference on Intelligent User InterfacesWhile conventional Recommender Systems perform well in automatically generating personalized suggestions, it is often difficult for users to understand why certain items are recommended and which parts of the item space are covered by the ...
Improving Collaborative Filtering Based Recommenders Using Topic Modelling
WI-IAT '14: Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 01Standard Collaborative Filtering (CF) algorithms make use of interactions between users and items in the form of implicit or explicit ratings alone for generating recommendations. Similarity among users or items is calculated purely based on rating ...
Enhancing User Personalization in Conversational Recommenders
WWW '23: Proceedings of the ACM Web Conference 2023Conversational recommenders are emerging as a powerful tool to personalize a user’s recommendation experience. Through a back-and-forth dialogue, users can quickly hone in on just the right items. Many approaches to conversational recommendation, ...
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