Christine Bauer
Markus Schedl
ABSTRACT
There is a long tradition in recommender systems research to evaluate systems using quantitative performance measures on fixed datasets. As a reaction to this narrow accuracy-based focus in research, novel qualities beyond pure accuracy are emphasized in recent research; among them are surprise and opposition.
This position paper considers that the perception of surprise and/or opposition may be purposely prepared when several recommendations are provided (e.g., in terms of a music playlist) or the user is given the choice between several options.
Altering users' perception and triggering according behavior is well rooted in research on priming from psychology and nudge theory from the field of economic behavior.
In this position paper, we propose how priming and nudging may be integrated into the design and evaluation of recommender systems to arouse surprise and opposition.
- Panagiotis Adamopoulos and Alexander Tuzhilin. 2014. On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected. ACM Transactions on Intelligent Systems and Technology 5, 4, Article 54 (Dec. 2014), 32 pages. Google Scholar
Digital Library
- John A. Bargh and Tanya L. Chartrand. 2000. The mind in the middle. Handbook of research methods in social and personality psychology (2000), 253--285.Google Scholar
- Thierry Bertin-Mahieux, Daniel P.W. Ellis, Brian Whitman, and Paul Lamere. 2011. The Million Song Dataset. In Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR). Miami, FL, USA.Google Scholar
- Dmitry Bogdanov. 2013. From music similarity to music recommendation: Computational approaches based in audio features and metadata. PhD. Universitat Pompeu Fabra, Barcelona, Spain.Google Scholar
- Óscar Celma and Perfecto Herrera. 2008. A New Approach to Evaluating Novel Recommendations. In Proceedings of the 2nd ACM Conference on Recommender Systems (RecSys). Lausanne, Switzerland, 8. Google ScholarDigital Library
- Charles L.A. Clarke, Maheedhar Kolla, Gordon V. Cormack, Olga Vechtomova, Azin Ashkan, Stefan Büttcher, and Ian MacKinnon. 2008. Novelty and Diversity in Information Retrieval Evaluation. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '08). ACM, New York, NY, USA, 659--666. Google ScholarDigital Library
- Isaac Dinner, Eric J. Johnson, Daniel G. Goldstein, and Kaiya Liu. 2011. Partitioning default effects: why people choose not to choose. Journal of Experimental Psychology: Applied 17, 4 (2011).Google Scholar
- Daniel G. Goldstein, Eric J. Johnson, Andreas Herrmann, and Mark Heitmann. 2008. Nudge your customers toward better choices. Harvard Business Review 86, 12 (2008), 99--105.Google Scholar
- Daniel M. Hausman and Brynn Welch. 2010. Debate: To nudge or not to nudge. Journal of Political Philosophy 18, 1 (2010), 123--136.Google ScholarCross Ref
- E. Tory Higgins, William S. Rholes, and Carl R. Jones. 1977. Category accessibility and impression formation. Journal of Experimental Social Psychology 13, 2 (1977), 141--154.Google ScholarCross Ref
- Eric J. Johnson and Daniel Goldstein. 2003. Medicine. Do defaults save lives? Science (New York, NY) 302, 5649 (2003), 1338--1339.Google Scholar
- Peter Knees, Kristina Andersen, Alan Said, and Marko Tkalcic. 2016. Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems (SOAP). In UMAP 2016 Extended Proceedings - Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems (SOAP) (July 16, 2016). ACM, New York, NY, USA.Google Scholar
- Peter Knees and Markus Schedl. 2013. A Survey of Music Similarity and Rec- ommendation from Music Context Data. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) 10, 1 (2013). Google ScholarDigital Library
- Mark Kosters and Jeroen Van der Heijden. 2015. From mechanism to virtue: Evaluating Nudge theory. Evaluation 21, 3 (2015), 276--291.Google ScholarCross Ref
- Karl Spencer Lashley. 1951. The problem of serial order in behavior. In Cerebral mechanisms in behavior. 112--136.Google Scholar
- Susan Michie and Robert West. 2013. Behaviour change theory and evidence: a presentation to Government. Health Psychology Review 7, 1 (2013), 1--22.Google ScholarCross Ref
- Tobias Mirsch, Christiane Lehrer, and Reinhard Jung. 2017. Digital Nudging: Altering User Behavior in Digital Environments. In Proceedings of13th International Conference on Wirtschaftsinformatik (WI '17). AIS.Google Scholar
- Alberto Novello, Martin F. McKinney, and Armin Kohlrausch. 2006. Perceptual Evaluation of Music Similarity. In Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR). Victoria, BC, Canada.Google Scholar
- Roger Ratcliff and Gail McKoon. 1996. Bias effects in implicit memory tasks. Journal of Experimental Psychology: General 125, 4 (1996), 403.Google ScholarCross Ref
- Markus Schedl. 2012. #nowplaying Madonna: A Large-Scale Evaluation on Estimating Similarities Between Music Artists and Between Movies from Microblogs. Information Retrieval 15 (June 2012), 183--217. Issue 3--4. Google ScholarDigital Library
- Markus Schedl. 2016. The LFM-1B Dataset for Music Retrieval and Recommendation. In Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval (ICMR '16). ACM, New York, NY, USA, 103--110. Google ScholarDigital Library
- Markus Schedl, Arthur Flexer, and Julián Urbano. 2013. The Neglected User in Music Information Retrieval Research. Journal of Intelligent Information Systems (July 2013). Google ScholarDigital Library
- Markus Schedl and David Hauger. 2015. Tailoring Music Recommendations to Users by Considering Diversity, Mainstreaminess, and Novelty. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '15). ACM, New York, NY, USA, 947--950. Google ScholarDigital Library
- Markus Schedl, David Hauger, and Dominik Schnitzer. 2012. A Model for Serendipitous Music Retrieval. In Proceedings of the 2nd Workshop on Context-awareness in Retrieval and Recommendation (CaRR). Lisbon, Portugal. Google ScholarDigital Library
- Lowell H. Storms. 1958. Apparent backward association: A situational effect. Journal of Experimental Psychology 55, 4 (1958), 390.Google ScholarCross Ref
- Cass R. Sunstein. 2014. Nudging: A very short guide. Journal of Consumer Policy 37, 4 (2014), 583--588.Google ScholarCross Ref
- Richard H. Thaler and Cass R. Sunstein. 2008. Nudge: Improving Decisions About Health, Wealth, and Happiness. (2008).Google Scholar
- Julián Urbano. 2013. Evaluation in Audio Music Similarity. Ph.D. Dissertation. University Carlos III of Madrid.Google Scholar
- Saúl Vargas and Pablo Castells. 2011. Rank and Relevance in Novelty and Diversity Metrics for Recommender Systems. In Proceedings of the 5th ACM Conference on Recommender Systems (RecSys). Chicago, IL, USA, 8. Google ScholarDigital Library
- Gabriel Vigliensoni and Ichiro Fujinaga. 2016. Automatic music recommendation systems: do demographic, profiling, and contextual features improve their performance?. In Proceedings of the 17th International Society for Music Information Retrieval Conference (August 7--11, 2016) (ISMIR '16). 94--100.Google Scholar
- Markus Weinmann, Christoph Schneider, and Jan vom Brocke. 2016. Digital Nudging. Business & Information Systems Engineering 58, 6 (2016), 433--436.Google ScholarCross Ref
- Peter Wright. 2002. Marketplace Metacognition and Social Intelligence. Journal of Consumer Research 28, 4 (2002), 677.Google ScholarCross Ref
- Mi Zhang and Neil Hurley. 2008. Avoiding Monotony: Improving the Diversity of Recommendation Lists. In Proceedings of the 2 nd ACM Conference on Recommender Systems (RecSys). Lausanne, Switzerland, 8. Google ScholarDigital Library
- Yuan Cao Zhang, Diarmuid Ó. Séaghdha, Daniele Quercia, and Tamas Jambor. 2012. Auralist: Introducing Serendipity into Music Recommendation. In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining (WSDM '12). ACM, New York, NY, USA, 13--22. Google ScholarDigital Library
Index Terms
- Introducing Surprise and Opposition by Design in Recommender Systems
Recommendations
Improving Accuracy of Recommender System by Item Clustering
Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches ...
An Adaptive Recommender System for Computational Serendipity
ICTIR '18: Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information RetrievalSerendipity is recognized as very challenging to simulate and stimulate in recommender systems. In this paper, we adopt a novel approach to model and implement serendipity in a context of health news recommender system. The proposed conceptual framework ...
A New Approach for Recommender System
ICACS '17: Proceedings of the 1st International Conference on Algorithms, Computing and SystemsIn today's e-commerce environment, Collaborative Filtering (CF) is a widely used algorithm for recommender system, which is to identify the users who have similar preferences to the target user, and to predict the preference of the target user according ...
Comments