Improved SVD + + Recommendation Algorithm Based on Fusion Time Factor

Wentao Zhao, Ziheng Cui, Tingting Feng

Abstract


Collaborative filtering algorithm is widely used in recommendation system. Aiming at the problems of data sparsity and low recommendation accuracy in traditional collaborative filtering algorithm, an improved recommendation algorithm is proposed PT _ SVD++. Firstly, the attribute information of users and the implicit feedback information of items are introduced to improve the SVD++ algorithm, which solves the insufficient utilization of information and alleviates the problem of sparse data;Secondly the time effect model is established to further improve the accuracy of the prediction results. The experimental results on MovieLens dataset show that compared with other algorithms, the average absolute error and root mean square error of this algorithm are lower, and its recommendation accuracy is higher.

Keywords


SVD + + algorithm; Time effect; Recommendation algorithm

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References


Ren YG, Zhang YP, Zhang ZP. Collaborative filtering recommendation algorithm based on rough set rule extraction[J]. Journal on Communications, 2020,41(1):76-83.

Agrawal S, Jain P.An improved approach for movie recommendation system[C]// International Conference on loT in Social, Mobile, Analytics and Cloud (I-SMAC), Palladam, 2017:336-342.

Huang LW, Jiang BT, Lyu SY, et al. Survey on deep learning based recommender systems[J]. Chinese Journal of Computer, 2018, 41(7):1619-1647.

Zhang KH, Liang JY, Zhao XW, et al. A collaborative filtering recommendation algorithm based on community expert information [J], Computer research and development, 2018, 55 (5): 968-976.

Mei LX, Yu X. Representation learning method for implicit feedback recommendation system [J], Computer application research, 2020, 37(8): 2266- 2272.

Zhu AY, Ren XJ. Regularized recommendation model based on user trust and score bias [J]. Modern computer, 2018, 615(15):30-35.

Kumar R,Verma BK, Rastogi SS. Social Popularity based SVD++ Recommender System[J]. International Journal of Computer Applications, 2014, 87(14):33-37.




DOI: http://dx.doi.org/10.18686/esta.v9i3.275

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