Abstract
User-based collaborative filtering (CF) is a widely used recommendation method that suggests items to users based on ratings of other users in the system. The performance of user-based CF can be degraded due to its inherent weaknesses, such as data sparsity and cold start problems. To address these weaknesses, many researchers have proposed to incorporate trust information into user-based CF. However, as reported in many recent works on trust aware recommendation, effectively exploiting trust in recommendation is not straightforward due to insufficient understanding of the relationship between trust and ratings. This paper empirically analyses real-world ratings data and their associated trust networks. Specifically, we focus our analysis on comparative characteristics of cold users vs. non-cold users. Our results show that the characteristics of cold users and non-cold users are significantly different.
This work was supported by KMITL-UEC Global Alliance Lab (KMITL-UEC GAL). The KMITL-UEC GAL was established in 2014 under the collaboration between King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand and the University of Electro-Communications, Tokyo, Japan.
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Somboonviwat, K., Aoyama, H. (2016). Empirical Analysis of the Relationship Between Trust and Ratings in Recommender Systems. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49381-6_12
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DOI: https://doi.org/10.1007/978-3-662-49381-6_12
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