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A hybrid user-based collaborative filtering algorithm with topic model

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Abstract

Currently available Collaborative Filtering(CF) algorithms often utilize user behavior data to generate recommendations. The similarity calculation between users is mostly based on the scores, without considering the explicit attributes of the users with profiles, as these are difficult to generate, or their evolution of preferences over time. This paper proposes a collaborative filtering algorithm named T-LDA (Time-decay Dirichlet Allocation), which is based on the topic model. In this method, we generate a hybrid score for similarity calculation with topic model. However, most topic models ignore the attribute of time order. In order to further improve the prediction accuracy, a time-decay function is introduced in topic model. The experimental results show that this algorithm has better performance than currently available algorithms on the MovieLens dataset, Netflix dataset and la.fm dataset.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (No.61402069), National key research and development plan (NO.2017YFC0821003), General project of Liaoning Provincial Department of Education Science Research(NO.L2015047), Natural Science Foundation of Liaoning Province (No.20180550395).

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Authors and Affiliations

  1. School of Information Science and Engineering, Dalian Polytechnic University, Dalian, China

    Liu Na, Li Ming-xia, Qiu Hai-yang & Su Hao-long

  2. University of Chinese Academy of Sciences, Beijing, China

    Li Ming-xia

  3. Shenyang Institute of Computing Technology, Shenyang, China

    Li Ming-xia

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  1. Liu Na
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  2. Li Ming-xia
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  3. Qiu Hai-yang
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  4. Su Hao-long
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Correspondence to Liu Na.

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Na, L., Ming-xia, L., Hai-yang, Q. et al. A hybrid user-based collaborative filtering algorithm with topic model. Appl Intell 51, 7946–7959 (2021). https://doi.org/10.1007/s10489-021-02207-7

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