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Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Social recommendation based on matrix factorization enhances the learning accuracy by adding user trust relationship, but ignored the influence of related information between items on user interest in the process of model decomposition. This paper first provided an improved item similarity function to measure correlations between items by considering the user frequency factor, and then established a matrix factorization model with two regularization terms, which combined item relations regularization and social regularization into matrix factorization objective function to present the constraints on low rank approximation. It explored a major impact of the item association information on recommendation when optimizing the implicit feature matrix. The experiments on two real-world datasets demonstrate that this method outperforms other state-of-the-art matrix factorization approaches especially dealing with the large sparse data, and effectively alleviates the cold-start user problem. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)