Improved SVD + + Recommendation Algorithm Based on Fusion Time Factor

Wentao Zhao, Ziheng Cui, Tingting Feng


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.


SVD + + algorithm; Time effect; Recommendation algorithm

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