Abstract
The collaborative filtering (CF) approach is one of the most successful personalized recommendation methods so far, which is employed by the majority of personalized recommender systems to predict usersโ preferences or interests. The basic idea of CF is that if users had the same interests in the past they will also have similar tastes in the future. In general, the traditional CF may suffer the following problems: (1) The recommendation quality of CF based system is greatly affected by the sparsity of data. (2) The traditional CF is relatively difficult to adapt the situation that usersโ preferences always change over time. (3) CF based approaches are used to recommend similar items to a user ignoring the userโs demand for variety. In this paper, to solve the above problems we build a new user-user covariance matrix to replace the traditional CFโs user-user similarity matrix. Compared with the user-user similarity matrix, the user-user covariance matrix introduces the user-user covariance to finely describe the changing trends of usersโ interests. Furthermore, we propose an enhancing collaborative filtering method based on the user-user covariance matrix. The experimental results show that the proposed method can significantly improve the diversity of recommendation results and ensure the good recommendation precision.
Highlights
Recommendation systems have been widely applied to deal with information overload problems in e-commerce sites [1]
We propose an enhancing collaborative filtering method based on the user-user covariance matrix
It is well known that collaborative filtering (CF) is one of the most widely used methods in recommender systems
Summary
Recommendation systems have been widely applied to deal with information overload problems in e-commerce sites [1]. It is well known that collaborative filtering (CF) is one of the most widely used methods in recommender systems. CF utilizes usersโ behaviors (e.g., ratings or clicks) to infer a target userโs preference for a particular item. CF approach has been employed by the majority of traditional personalized recommender systems, CF approach usually faces the following challenges: (1) The sparse data of the user-item matrix seriously affect the recommendation quality. (3) Always recommending similar items to a user will fail to meet the userโs demand for variety. We propose an enhancing collaborative f iltering method based on covariance matrix named CFCM, such that the problems described above can be solved. Mathematical Problems in Engineering covariance matrix and presents an enhancing collaborative filtering method.
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