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Collaboration Matrix Factorization on Rate and Review for Recommendation

Collaboration Matrix Factorization on Rate and Review for Recommendation

Zhicheng Wu, Huafeng Liu, Yanyan Xu, Liping Jing
Copyright: © 2019 |Volume: 30 |Issue: 2 |Pages: 17
ISSN: 1063-8016|EISSN: 1533-8010|EISBN13: 9781522563792|DOI: 10.4018/JDM.2019040102
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MLA

Wu, Zhicheng, et al. "Collaboration Matrix Factorization on Rate and Review for Recommendation." JDM vol.30, no.2 2019: pp.27-43. http://doi.org/10.4018/JDM.2019040102

APA

Wu, Z., Liu, H., Xu, Y., & Jing, L. (2019). Collaboration Matrix Factorization on Rate and Review for Recommendation. Journal of Database Management (JDM), 30(2), 27-43. http://doi.org/10.4018/JDM.2019040102

Chicago

Wu, Zhicheng, et al. "Collaboration Matrix Factorization on Rate and Review for Recommendation," Journal of Database Management (JDM) 30, no.2: 27-43. http://doi.org/10.4018/JDM.2019040102

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Abstract

According to the sparseness of rating information, the quality of recommender systems has been greatly restricted. In order to solve this problem, much auxiliary information has been used, such as social networks, review information, and item description. Convolutional neural networks (CNNs) have been widely employed by recommender systems, it greatly improved the rating prediction's accuracy especially when combined with traditional recommendation methods. However, a large amount of research focuses on the consistency between the rating-based latent factor and review-based latent factor. But in fact, these two parts are completely different. In this article, the authors propose a model named collaboration matrix factorization (CMF) that combines a projection method with a convolutional matrix factorization (ConvMF) to extract the collaboration between rating-based latent factors and review-based latent factors that comes from the results of the CNN process. Extensive experiments on three real-world datasets show that the projection method achieves significant improvements over the existing baseline.

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