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Collaborative filtering via co-factorization of individuals and groups | IEEE Conference Publication | IEEE Xplore

Collaborative filtering via co-factorization of individuals and groups


Abstract:

Matrix factorization is one of the most successful collaborative filtering methods for recommender systems. Traditionally, matrix factorization only uses the observed use...Show More

Abstract:

Matrix factorization is one of the most successful collaborative filtering methods for recommender systems. Traditionally, matrix factorization only uses the observed user-item feedback information, which makes predictions on cold users/items difficult. In many applications, user/item content information are also available and they have been successfully used in content-based methods. In recent years, there are attempts to incorporate content information into matrix factorization. In particular, the Factorization Machine (FM) is one of the most notable examples. However, FM is a general factorization model that models interactions between all features into a latent feature space. In this paper, we propose a novel combination of tree-based feature group learning and matrix co-factorization that extends FM to recommender systems. Experimental results on a number of benchmark data sets show that the proposed algorithm outperforms state-of-the-art methods, particularly for predictions on cold users and cold items.
Date of Conference: 12-17 July 2015
Date Added to IEEE Xplore: 01 October 2015
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Conference Location: Killarney, Ireland

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