Abstract
Online services are usually accessed via household accounts. A household account is typically shared by various users who live in the same house. This represents a problem for providing personalized services, such as recommendation. Identifying the household members who are interacting with an online system (e.g. an on-demand video service) in a given moment, is thus an interesting challenge for the recommender systems research community. Previous work has shown that methods based on the analysis of temporal patterns of users are highly accurate in the above task when they use randomly sampled test data. However, such evaluation methodology may not properly deal with the evolution of the users’ preferences and behavior through time. In this paper we evaluate several methods’ performance using time-aware evaluation methodologies. Results from our experiments show that the discrimination power of different time features varies considerably, and moreover, the accuracy achieved by the methods can be heavily penalized when using a more realistic evaluation methodology.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kabutoya, Y., Iwata, T., Fujimura, K.: Modeling Multiple Users’ Purchase over a Single Account for Collaborative Filtering. In: Chen, L., Triantafillou, P., Suel, T. (eds.) WISE 2010. LNCS, vol. 6488, pp. 328–341. Springer, Heidelberg (2010)
Berkovsky, S., De Luca, E.W., Said, A.: Challenge on Context-Aware Movie Recommendation: CAMRa2011. In: Proceedings of the 5th ACM Conference on Recommender Systems, pp. 385–386 (2011)
Campos, P.G., Bellogin, A., Díez, F., Cantador, I.: Time feature Selection for Identifying Active Household Members. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 2311–2314 (2012)
Zhang, M., Hurley, N.: Avoiding Monotony: Improving the Diversity of Recommendation Lists. In: Proceedings of the 2nd ACM Conference on Recommender Systems, pp. 123–130 (2008)
Campos, P.G., Díez, F., Bellogín, A.: Temporal Rating Habits: A Valuable Tool for Rater Differentiation. In: Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation, pp. 29–35 (2011)
Bento, J., Fawaz, N., Montanari, A., Ioannidis, S.: Identifying Users from Their Rating Patterns. In: Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation, pp. 39–46 (2011)
Campos, P.G., Díez, F., Cantador, I.: Time-Aware Recommender Systems: A Comprehensive Survey and Analysis of Existing Evaluation Protocols. User Modeling and User-Adapted Interaction (in press, 2013)
Ardissono, L., Portis, F., Torasso, P., Bellifemine, F., Chiarotto, A., Difino, A.: Architecture of a System for the Generation of Personalized Electronic Program Guides. In: Proceedings of the UM 2001 Workshop on Personalization in Future TV (2001)
Vildjiounaite, E., Kyllönen, V., Hannula, T., Alahuhta, P.: Unobtrusive dynamic modelling of TV program preferences in a household. In: Tscheligi, M., Obrist, M., Lugmayr, A. (eds.) EuroITV 2008. LNCS, vol. 5066, pp. 82–91. Springer, Heidelberg (2008)
Goren-Bar, D., Glinansky, O.: FIT-recommending TV Programs to Family Members. Computers & Graphics 24, 149–156 (2004)
Oh, J., Sung, Y., Kim, J., Humayoun, M., Park, Y.-H., Yu, H.: Time-Dependent User Profiling for TV Recommendation. In: Proceedings of the 2nd International Conference on Cloud and Green Computing, pp. 783–787 (2012)
Shi, Y., Larson, M., Hanjalic, A.: Mining Relational Context-aware Graph for Rater Identification. In: Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation, pp. 53–59 (2011)
Lathia, N., Hailes, S., Capra, L.: Temporal Collaborative Filtering with Adaptive Neighbourhoods. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 796–797 (2009)
Campos, P.G., Díez, F., Sánchez-Montañés, M.: Towards a More Realistic Evaluation: Testing the Ability to Predict Future Tastes of Matrix Factorization-based Recommenders. In: Proceedings of the 5th ACM Conference on Recommender Systems, pp. 309–312 (2011)
Shani, G., Gunawardana, A.: Evaluating Recommendation Systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 257–297. Springer US (2011)
Kohavi, R., Longbotham, R., Sommerfield, D., Henne, R.M.: Controlled Experiments on the Web: Survey and Practical Guide. Data Mining and Knowledge Discovery 18, 140–181 (2008)
Bennett, J., Lanning, S.: The Netflix Prize. In: Proceedings of KDD Cup and Workshop (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Campos, P.G., Bellogín, A., Cantador, I., Díez, F. (2013). Time-Aware Evaluation of Methods for Identifying Active Household Members in Recommender Systems. In: Bielza, C., et al. Advances in Artificial Intelligence. CAEPIA 2013. Lecture Notes in Computer Science(), vol 8109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40643-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-40643-0_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40642-3
Online ISBN: 978-3-642-40643-0
eBook Packages: Computer ScienceComputer Science (R0)