Letters: Conceptual collaborative filtering recommendation: A probabilistic learning approach

Authors:
Jae-won Lee;Han-Joon Kim;Sang-goo Lee
Affiliations:
School of Computer Science and Engineering, Seoul National University, Republic of Korea;School of Electrical and Computer Engineering, The University of Seoul, Republic of Korea;School of Computer Science and Engineering, Seoul National University, Republic of Korea
Venue:
Neurocomputing
Year:
2010

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Abstract

Collaborative filtering is one of the most successful and popular methods in developing recommendation systems. However, conventional collaborative filtering methods suffer from item sparsity and new item problems. In this paper, we propose a probabilistic learning approach that solves the item sparsity problem while describing users and items with domain concepts. Our method uses a probabilistic match with domain concepts, whereas conventional collaborative filtering uses an exact match to find similar users. Empirical experiments show that our method outperforms the conventional ones.