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A Matrix Factorization Recommendation Method with Tags and Contents |
Ma Yingxue,Gan Mingxin(),Xiao Kejun |
School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China |
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Abstract [Objective] This paper proposes a matrix factorization method (TCMF) integrating tags and contents, aiming to address the issue of heterogeneous information fusion in recommendation system. It tries to reduce prediction errors, overcome the problem of data sparsity, and improve the robustness of matrix factorization algorithm. [Methods] We transformed textual message to structured data with the help of embedding. Then, we extracted hidden features with CNN. Third, we merged the features of movie contents and tags with DNN to obtain comprehensive features. Finally, we proposed the TCMF based on matrix factorization algorithm and evaluated its performance with movie rating dataset (MovieLens-20m). [Results] The TCMF reduced the error of movie rating predictions (with the lowest RMSE of 0.829 5 and the lowest MAE of 0.618 9). Compared with the exisiting methods, the maxium reduction of RMSE and MAE were 9.62% and 14.17%. [Limitations] Due to the lack of information, the TCMF cannot characterize users’ personalized features. [Conclusions] The proposed model not only reduces the error of rating prediction, but also improves robustness of algorithm.
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Received: 26 October 2020
Published: 27 May 2021
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Fund:The work is supported by the National Natural Science Foundation of China(71871019);The work is supported by the National Natural Science Foundation of China(71471016) |
Corresponding Authors:
Gan Mingxin
E-mail: ganmx@ustb.edu.cn
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