Efficient solution of the SVD recommendation model with implicit feedback

Abstract

Collaborative filtering, an important part of the recommendation system, has become a mainstream recommendation method. In collaborative filtering methods based on potential factors, SVD recommendation models are often used to analyze user preferences. With the recent research of SVD recommendation models, some SVD recommendation models with implicit feedback, such as SVD+ and TrustSVD, have been successively proposed. These types of models can more effectively mine useful information from limited data sources and achieve better results than traditional SVD recommendation model, thereby garnering widespread attention. However, most existing papers focus on model design and the lack of efficient algorithms for SVD recommendation models with implicit feedback. Therefore, this paper first studies the general gradient solution framework of the SVD recommendation model. Then, it considers the SVD+ recommendation model as a breakthrough and designs an efficient solution algorithm, namely, BCDSVD+, based on the block coordinate descent method. Furthermore, we solve the two key problems of capacity matrix inversion and sparse data optimization processing. Experiments show that the proposed BCDSVD+ algorithm yields better solution efficiency and convergence ability than the traditional parallel gradient descent method.