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. 2023 Jul 12:9:e1448.
doi: 10.7717/peerj-cs.1448. eCollection 2023.

Personalized movie recommendations based on deep representation learning

Luyao Li  1 , Hong Huang  1 , Qianqian Li  2 , Junfeng Man  1   3   4
Affiliations

Personalized movie recommendations based on deep representation learning

Luyao Li et al. PeerJ Comput Sci. .

Abstract

Personalized recommendation is a technical means to help users quickly and efficiently obtain interesting content from massive information. However, the traditional recommendation algorithm is difficult to solve the problem of sparse data and cold-start and does not make reasonable use of the user-item rating matrix. In this article, a personalized recommendation method based on deep belief network (DBN) and softmax regression is proposed to address the issues with traditional recommendation algorithms. In this method, the DBN is used to learn the deep representation of users and items, and the user-item rating matrix is maximized. Then softmax regression is used to learn multiple categories in the feature space to predict the probability of interaction between users and items. Finally, the method is applied to the area of movie recommendation. The key to this method is the negative sampling mechanism, which greatly improves the effectiveness of the recommendations, as a result, creates an accurate list of recommendations. This method was verified and evaluated on Douban and several movielens datasets of different sizes. The experimental results demonstrate that the recommended performance of this model, which has high accuracy and generalization ability, is much better than typical baseline models such as singular value decomposition (SVD), and the mean absolute error (MAE) value is 98%, which is lower than the best baseline model.

Keywords: Collaborative filtering; DBN; Representation learning; Sampling softmax; Recommendation system.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. RBM structure diagram.
Figure 2
Figure 2. DBM structure diagram.
Figure 3
Figure 3. Structure diagram of softmax.
Figure 4
Figure 4. The framework of the proposed recommendation algorithm.
Figure 5
Figure 5. Schematic diagram of the number of RBN layers.
Figure 6
Figure 6. Convergence of DBN at different learning rates.
Figure 7
Figure 7. Ablation experiment.
Figure 8
Figure 8. A comparison of the MAE values of several models.
Figure 9
Figure 9. A comparison of the precision of several models.
Figure 10
Figure 10. Performance of the model on three Movielens datasets.
Figure 11
Figure 11. Effects of the model on Movielens_25m dataset and douban dataset.
See this image and copyright information in PMC

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References

    1. Ahmadian S, Ahmadian M, Jalili M. A deep learning based trust-and tag-aware recommender system. Neurocomputing. 2022;488:557–571. doi: 10.1016/j.neucom.2021.11.064. - DOI
    1. Bi JW, Liu Y, Fan ZP. A deep neural networks based recommendation algorithm using user and item basic data. International Journal of Machine Learning and Cybernetics. 2020;11(4):763–777. doi: 10.1007/s13042-019-00981-y. - DOI
    1. Chicco D, Warrens MJ, Jurman G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science. 2021;7:e623. doi: 10.7717/peerj-cs.623. - DOI - PMC - PubMed
    1. Covington P, Adams J, Sargin E. Deep neural networks for youtube recommendations. Proceedings of the 10th ACM conference on recommender systems; 2016. pp. 191–198.
    1. Cui Z, Xu X, Fei XUE, Cai X, Cao Y, Zhang W, Chen J. Personalized recommendation system based on collaborative filtering for IoT scenarios. IEEE Transactions on Services Computing. 2020;13(4):685–695. doi: 10.1109/TSC.2020.2964552. - DOI

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