ABSTRACT
There is an increasing consensus in the field of recommender systems that we should move beyond the offline evaluation of algorithms towards a more user-centric approach. This tutorial teaches the essential skills involved in conducting user experiments, the scientific approach to user-centric evaluation. Such experiments are essential in uncovering how and why the user experience of recommender systems comes about.
- Adomavicius, G. and Tuzhilin, A. 2011. Context-Aware Recommender Systems. Recommender Systems Handbook. F. Ricci, L. Rokach, B. Shapira, and P.B. Kantor, eds. Springer US. 217--253. Google ScholarDigital Library
- Bollen, D., Knijnenburg, B.P., Willemsen, M.C. and Graus, M. 2010. Understanding choice overload in recommender systems. Proceedings of the fourth ACM conference on Recommender systems (Barcelona, Spain, 2010), 63--70. Google ScholarDigital Library
- Knijnenburg, B.P., Reijmer, N.J.M. and Willemsen, M.C. 2011. Each to his own: how different users call for different interaction methods in recommender systems. Proceedings of the fifth ACM conference on Recommender systems (Chicago, IL, 2011), 141--148. Google ScholarDigital Library
- Knijnenburg, B.P., Schmidt-Thieme, L. and Bollen, D.G.F.M. 2010. Workshop on user-centric evaluation of recommender systems and their interfaces. Proceedings of the fourth ACM conference on Recommender systems (New York, NY, USA, 2010), 383--384. Google ScholarDigital Library
- Knijnenburg, B.P. and Willemsen, M.C. 2009. Understanding the effect of adaptive preference elicitation methods on user satisfaction of a recommender system. Proceedings of the third ACM conference on Recommender systems (New York, NY, 2009), 381--384. Google ScholarDigital Library
- Knijnenburg, B.P., Willemsen, M.C., Gantner, Z., Soncu, H. and Newell, C. 2012. Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction. 22, 4--5 (2012), 441--504. Google ScholarDigital Library
- Knijnenburg, B.P., Willemsen, M.C. and Kobsa, A. 2011. A pragmatic procedure to support the user-centric evaluation of recommender systems. Proceedings of the fifth ACM conference on Recommender systems (New York, NY, USA, 2011), 321--324. Google ScholarDigital Library
- Konstan, J. and Riedl, J. 2012. Recommender systems: from algorithms to user experience. User Modeling and User-Adapted Interaction. 22, 1 (2012), 101--123. Google ScholarDigital Library
- McNee, S.M., Riedl, J. and Konstan, J.A. 2006. Being accurate is not enough. CHI '06 extended abstracts on Human factors in computing systems (Montreal, Quebec, Canada, 2006), 1097--1101. Google ScholarDigital Library
- Pu, P., Chen, L. and Hu, R. 2012. Evaluating recommender systems from the user's perspective: survey of the state of the art. User Modeling and User-Adapted Interaction. 22, 4 (2012), 317--355. Google ScholarDigital Library
- Willemsen, M., Bollen, D. and Ekstrand, M. 2011. UCERSTI 2: second workshop on user-centric evaluation of recom-mender systems and their interfaces. Proceedings of the fifth ACM conference on Recommender systems (New York, NY, USA, 2011), 395--396. Google ScholarDigital Library
Index Terms
- Conducting user experiments in recommender systems
Recommendations
A pragmatic procedure to support the user-centric evaluation of recommender systems
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