Abstract
Since there have been many practical recommendation services in real world, main research questions are i) how such services provide users with recommendations, and ii) how they are different from each other. The aim of this paper is to evaluate user modeling process in several practical recommendation systems. Black-box testing scheme has been applied by comparing recommendation results. User models (i.e., a set of user ratings) have been synthesized to discriminate the recommendation results. Particularly, we focus on investigating whether the services consider attribute selection.
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
Pu, P., Chen, L., Hu, R.: Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Modeling and User-Adapted Interaction 22(4-5), 317–355 (2012)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)
Jung, J.J.: Attribute selection-based recommendation framework for short-head user group: An empirical study by movielens and imdb. Expert Systems with Applications 39(4), 4049–4054 (2012)
Beizer, B.: Black-Box Testing: Techniques for Functional Testing of Software and Systems. John Wiley & Sons (1995)
Reisinger, D.: Top 10 movie recommendation engines (March 2009)
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
Pham, X.H., Luong, T.N., Jung, J.J. (2013). An Black-Box Testing Approach on User Modeling in Practical Movie Recommendation Systems. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2013. Lecture Notes in Computer Science(), vol 8083. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40495-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-40495-5_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40494-8
Online ISBN: 978-3-642-40495-5
eBook Packages: Computer ScienceComputer Science (R0)