Collaborative recommendation method based on social context

Abstract

The invention discloses a collaborative recommendation method based on social context regularization. The collaborative recommendation method comprises the following steps of: 1) firstly, extracting a user object matrix and a socialization relation matrix, wherein during the collaborative recommendation, the user object matrix is defined by using a grading matrix of a user on an object, a clicking frequency of the user on the object or a visit relation, and the socialization relation is a relation, generated by some behaviors of the user, between the user and other users in the system; 2) filling the user object matrix by using a low-rank matrix decomposition method with the social context regularization and recommending N objects to each user by using a result matrix; and 3) adjusting the weight of the social context restraint during matrix decomposition in the consideration of difference among different users. By the method, the problems of single recommended information of the conventional collaborative filtering recommendation algorithm and inaccurate recommendation result caused by dilution of the user object matrix are solved; furthermore, compared with the conventional method, the method has the advantage of obviously enhancing the recommendation result accuracy.

Description

technical field [0001] The present invention relates to the field of personalized recommendation, in particular to a collaborative recommendation method based on social relationship. Background technique [0002] In recent years, the rapid development of the Internet has resulted in a rapid increase in the total amount of information on the Internet, and at the same time, e-commerce is also continuously expanding. The huge amount of online data causes users to spend a lot of time looking for their favorite items. This process of eliminating a large amount of useless information will undoubtedly hinder users from enjoying the convenience brought by the Internet. In order to solve these problems, the application of personalized recommendation system was born. The personalized recommendation system is an advanced intelligent platform based on massive data mining. It mainly recommends information and products of interest to users based on their interests and other information, ...

AI Technical Summary

Problems solved by technology

Although it has a wide range of applications, collaborative filtering recommendation is still difficult to overcome the problems caused by data sparsity and single source of information. The sparseness of user-item matrix leads to inaccurate calculation of user similarity, which affects the accuracy of recommendation, and only using a single Mining and recommending the user's item rating matrix can not overcome the problem caused by the insufficient information of a single rating matrix, thus affecting the recommendation results

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