Social recommending method based on classification

Abstract

The invention discloses a social recommending method based on classification. The social recommending method comprises the steps that an evaluation matrix of a user on classification is constructed; an initial user classification matrix is constructed according to evaluation data of the user on a project and classification information of the project; normalization processing is carried out on the initial user classification matrix, and the user classification matrix is reconstructed through a matrix decomposition method; the credibility between the friends of the user is constructed through the friend information of the user; the parameters of an obtained model are learnt and obtained through a stochastic gradient descent method according to the evaluation of the user on the classification of the project in the user classification matrix, the evaluation of the user on the project predicted and obtained through a socialization model and the credibility between the friends of the user, and therefore the final evaluation of the user on the project is predicted. According to the method, project classification information is guided into the social recommending method for the first time, the socialization information of the user and the classification information of the project are integrated on the basis of original collaborative filtering recommendation, recommending precision is improved, and the problems of data sparseness and cold starting in a recommending system are solved.

Description

technical field [0001] The invention belongs to the field of data mining and recommendation systems, and more specifically relates to a classification-based social recommendation method. Background technique [0002] Collaborative filtering is the most widely used and most successful technology in the recommendation system. It can use the past behavior or opinions of existing user groups to predict which things current users are most likely to like or are interested in. It is based on the assumption that "you are likely to like things that are liked by people with similar preferences to you". Collaborative filtering recommendation is generally divided into memory-based recommendation and model-based recommendation. Memory-based algorithms are the most basic algorithms in recommender systems, which have not only been deeply studied in academia, but also widely used in industry. Memory-based recommendation can be divided into user-based collaborative filtering and item-based...

AI Technical Summary

Problems solved by technology

[0008] Aiming at the deficiencies of the prior art, the present invention introduces project classification information into the social recommendation method for the first time, provides a classification-based social recommendation method, and aims to solve the problem of data sparseness and project coldness in the existing recommendation algorithms. Start problems, improve recommendation accuracy

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