Improved one-class collaborative filtering method based on socialized information fusion

Abstract

The invention discloses an improved one-class collaborative filtering method based on socialized information fusion. The improved one-class collaborative filtering method includes the steps of 1, constructing triples to respectively express user project information, project tag information, user tag information and group friend information; 2, computing preference similarity of friends in the group of the user; 3, computing feature similarity of the user's preference with his unselected project; 4, extracting and adding negative examples of the user; 5, subjecting user project matrix to joint probability matrix decomposition to obtain user feature matrix and project feature matrix; 6, acquiring the front N projects with highest prediction scores of each user to form a recommendation list of the user. Negative example extraction is carried out on the basis of project tag information and quality of positive examples selected by the user, socialized information of user groups and project tags are fused to the probability matrix decomposition to realize joint probability matrix decomposition, and accordingly, recommendation results to one-class data are acquired, and problems of height unbalancedness and sparsity of data caused in implementing of the conventional one-class collaborative filtering methods are solved effectively.

Description

technical field [0001] The invention belongs to the field of personalized recommendation, in particular to an improved single-category collaborative filtering method for fusing social information. Background technique [0002] With the continuous development of information technology, how to quickly and effectively find the information needed by users from massive data and meet the different individual needs of various users has attracted extensive attention of researchers. In this context, the recommendation system came into being. The recommendation system can learn and predict the user's preference based on the user's historical rating data to recommend items. It is considered to be one of the most effective methods to solve information overload. [0003] Among them, the most widely used is the recommendation algorithm based on collaborative filtering. The core of the collaborative filtering algorithm is based on the score data of the target user's nearest neighbors to ...

AI Technical Summary

Problems solved by technology

Although these studies have reduced the impact of data imbalance or sparsity on the recommendation results to a certain extent, on the one hand, most of the existing studies are carried out from a single perspective, only considering the impact of data imbalance and sparsity in isolation. Improvement of a question of sex

On the other

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