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Deep Plot-Aware Generalized Matrix Factorization for Collaborative Filtering

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Abstract

Fusing auxiliary information into ratings has shown promising performance for many recommendation tasks, such as age, sex, vocation of users or actors, director, genre, reviews of movies. However, all above auxiliary information is still sparse and not informative. For movie recommendations, besides the above information, there exists richer information in plot texts, exerting huge impacts on improving the recommendation accuracy. In this paper, we explore effective fusion of movie ratings and plot texts, we propose a deep plot-aware generalized matrix factorization for collaborative filtering model, which effectively combines both ratings and plot texts to implement a generalized collaborative filtering. To verify our proposal, we conduct extensive experiments on two popular datasets, and the results perform better than other state-of-the-art approaches in common recommendation tasks.

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Funding

Funding was provided by National Natural Science Foundation of China (Grant Nos. 71473035, 11501095).

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Authors and Affiliations

  1. Northeast Normal University, Changchun, China

    Xiaoxin Sun, Haobo Zhang, Meiqi Wang, Mengying Yu, Minghao Yin & Bangzuo Zhang

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  1. Xiaoxin Sun
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  2. Haobo Zhang
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  3. Meiqi Wang
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  4. Mengying Yu
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  5. Minghao Yin
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  6. Bangzuo Zhang
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Correspondence to Xiaoxin Sun.

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Sun, X., Zhang, H., Wang, M. et al. Deep Plot-Aware Generalized Matrix Factorization for Collaborative Filtering. Neural Process Lett 52, 1983–1995 (2020). https://doi.org/10.1007/s11063-020-10333-5

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