Abstract
With the development of social networks, the research of integrated social information recommendation models has received extensive attention. However, most existing social recommendation models are based on the matrix factorization technique which ignore the impact of the relationships between items on users’ interests, resulting in a decline of recommendation accuracy. To solve this problem, this paper proposes a double regularization matrix factorization recommendation algorithm. The algorithm first uses attribute information and manifold learning to calculate similarity. Then, the matrix factorization model is constrained through the regularization of item association relations and user social relations. Experimental results on real datasets show that the proposed method can effectively alleviate problems such as cold start and data sparsity in the recommender system and improve the recommendation accuracy compared with those of existing methods.
Highlights
As an effective method for addressing information overload, the recommender system has become a hot spot of concern in academia and industry
Traditional recommendation methods can generally be divided into three categories: content-based methods, collaborative filtering methods, and hybrid methods
An increasing number of social recommendation [5]–[11] algorithms have used social information from social networks to solve the problems of data sparsity and cold start
Summary
As an effective method for addressing information overload, the recommender system has become a hot spot of concern in academia and industry. An increasing number of social recommendation [5]–[11] algorithms have used social information from social networks to solve the problems of data sparsity and cold start. Recommendation which is based on matrix factorization is the most widely used With this method, users with strong social relationships often have similar preferences. Most existing social recommendation models based on matrix factorization focus on the user’s friends or trust relationships and have ignored the impact of the relationships between items on the user’s preferences. A low-dimensional similarity is obtained according to the overall similarity of the items. (2) The similarity of the low-dimensional manifold is added to the Pearson correlation calculation method to improve the result and yield a comprehensive item similarity. (3) Item regularization is included in the social recommendation objective function, and the new objective function can enhance the constraints of the item feature matrix
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