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Information 2018, 9(6), 143; https://doi.org/10.3390/info9060143

An Extended-Tag-Induced Matrix Factorization Technique for Recommender Systems

1
State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, China
2
College of Information Science & Technology, Hainan University, Haikou 570228, China
*
Author to whom correspondence should be addressed.
Received: 24 April 2018 / Revised: 3 June 2018 / Accepted: 8 June 2018 / Published: 11 June 2018
(This article belongs to the Section Information Systems)
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Abstract

Social tag information has been used by recommender systems to handle the problem of data sparsity. Recently, the relationships between users/items and tags are considered by most tag-induced recommendation methods. However, sparse tag information is challenging to most existing methods. In this paper, we propose an Extended-Tag-Induced Matrix Factorization technique for recommender systems, which exploits correlations among tags derived by co-occurrence of tags to improve the performance of recommender systems, even in the case of sparse tag information. The proposed method integrates coupled similarity between tags, which is calculated by the co-occurrences of tags in the same items, to extend each item’s tags. Finally, item similarity based on extended tags is utilized as an item relationship regularization term to constrain the process of matrix factorization. MovieLens dataset and Book-Crossing dataset are adopted to evaluate the performance of the proposed algorithm. The results of experiments show that the proposed method can alleviate the impact of tag sparsity and improve the performance of recommender systems. View Full-Text
Keywords: recommender system; collaborative filtering; matrix factorization; social tag recommender system; collaborative filtering; matrix factorization; social tag
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Han, H.; Huang, M.; Zhang, Y.; Bhatti, U.A. An Extended-Tag-Induced Matrix Factorization Technique for Recommender Systems. Information 2018, 9, 143.

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