AN ONLINE EVALUATION OF EXPLICIT FEEDBACK MECHANISMS FOR RECOMMENDER SYSTEMS

Simon Dooms, Toon De Pessemier, Luc Martens

2011

Abstract

The success of a recommender system is not only determined by smart algorithm design, but also by the quality of user data and user appreciation. User data are collected by the feedback system that acts as the communication link between the recommender and the user. The proper collection of feedback is thus a key component of the recommender system. If designed incorrectly, worthless or too little feedback may be collected, leading to low-quality recommendations. There is however little knowledge on the influence that design of feedback mechanisms has on the willingness for users to give feedback. In this paper we study user behavior towards four different explicit feedback mechanisms that are most commonly used in online systems, 5-star rating (static and dynamic) and thumbs up/down (static and dynamic). We integrated these systems into a popular (10,000 visitors a day) cultural events website and monitored the interaction of users. In 6 months over 8000 ratings were collected and analyzed. Current results show that the distinct feedback systems resulted in different user interaction patterns. Finding the right technique to encourage user interaction may be one of the next big challenges recommender systems have to face.

References

  1. Amatriain, X., Pujol, J. M., Tintarev, N., and Oliver, N. (2009). Rate it again: increasing recommendation accuracy by user re-rating. In RecSys 7809: Proceedings of the third ACM conference on Recommender systems, pages 173-180, New York, NY, USA. ACM.
  2. Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12:331-370. 10.1023/A:1021240730564.
  3. Cosley, D., Lam, S., Albert, I., Konstan, J., and Riedl, J. (2003). Is seeing believing?: how recommender system interfaces affect users' opinions. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 585-592. ACM.
  4. Jawaheer, G., Szomszor, M., and Kostkova, P. (2010). Comparison of implicit and explicit feedback from an online music recommendation service. In HetRec 7810: Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems, pages 47-51, New York, NY, USA. ACM.
  5. Srinivas, K. K., Gutta, S., Schaffer, D., Martino, J., and Zimmerman, J. (2001). A multi-agent tv recommender. In proceedings of the UM 2001 workshop ”'Personalization in Future TV”78.
  6. Vintila, B., Palaghita, D., and Dascalu, M. (2010). A new algorithm for self-adapting web interfaces. In 6th International Conference on Web Information Systems and Technologies, pages 57-62.
  7. Yu, Z. and Zhou, X. (2004). Tv3p: an adaptive assistant for personalized tv. Consumer Electronics, IEEE Transactions on, 50(1):393-399.
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Paper Citation


in Harvard Style

Dooms S., De Pessemier T. and Martens L. (2011). AN ONLINE EVALUATION OF EXPLICIT FEEDBACK MECHANISMS FOR RECOMMENDER SYSTEMS . In Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-8425-51-5, pages 391-394. DOI: 10.5220/0003330403910394


in Bibtex Style

@conference{webist11,
author={Simon Dooms and Toon De Pessemier and Luc Martens},
title={AN ONLINE EVALUATION OF EXPLICIT FEEDBACK MECHANISMS FOR RECOMMENDER SYSTEMS},
booktitle={Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2011},
pages={391-394},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003330403910394},
isbn={978-989-8425-51-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - AN ONLINE EVALUATION OF EXPLICIT FEEDBACK MECHANISMS FOR RECOMMENDER SYSTEMS
SN - 978-989-8425-51-5
AU - Dooms S.
AU - De Pessemier T.
AU - Martens L.
PY - 2011
SP - 391
EP - 394
DO - 10.5220/0003330403910394