LAUSR

The goal of a recommender system is to return related items that users may be interested in. However recommendation methods result in a sparsity problem that affects the generation of recommendation results and, thus, the user experience. Considering different user performance-related information in recommender systems, the recommendation models face new sparsity challenges. Specifically, the sparsity problem that existed in our previously proposed Product Attribute Model is due to the subjectivity of product reviews. When users comment on items, they do not include all aspects of the product. As a result, the user preference information acquired by the model is incomplete after data preprocessing. To solve this problem, a sparsity alleviation recommendation approach is presented in this paper that achieves a better product recommendation performance. The new sparsity alleviation algorithm for the recommendation model is designed to solve the sparsity problem by addressing the zero values. Based on the Multiplication Convergence Rule and Constraint Condition , the algorithm replaces zero values through equations. The sparsity problem of the Product Attribute Model can be alleviated in view of the accuracy of matrix factorization. We also propose a hybrid collaborative formula that incorporates product attribute information to generate better recommendation results. Experimental results on a sparsity dataset from Amazon demonstrate the effectiveness and applicability of our proposed recommendation approach, which outperforms a number of competitive baselines in both the within sparsity and without sparsity experiments.