Enhancing Collaborative Filtering by User-User Covariance Matrix

Abstract EN

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 users’ 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 users’ preferences always change over time.

(3) CF based approaches are used to recommend similar items to a user ignoring the user’s demand for variety.

In this paper, to solve the above problems we build a new user-user covariance matrix to replace the traditional CF’s 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 users’ 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.