Multiattribute collaborative filtering recommendation method oriented to social network

Abstract

The invention discloses a multiattribute collaborative filtering recommendation method oriented to a social network. The multiattribute collaborative filtering recommendation method includes utilizing mass data information of the social network to collect user, friend and item list information, and establishing an original user-item scoring matrix; utilizing a thought of acquiring a middle average value from nine numbers, and performing prediction filling on a sparse matrix; calculating inter-user attracting similarity through a user-item bipartite graph; calculating interaction similarity, linearly combining the attracting similarity with the interaction similarity to acquire comprehensive similarity among users, and searching to acquire a nearest neighbor set of a target user; performing prediction scoring on items to be recommended by the target user according to the nearest neighbor set of the target user, and generating a Top-N recommendation set. By the method, calculating rules of inter-user similarity in a conventional collaborative filtering method are improved, huge impedance brought to the filtering recommendation method and a recommendation system by sparseness of a scoring matrix is reduced, and accuracy of the recommendation system is improved.

Description

technical field [0001] The invention relates to the fields of data mining and information retrieval, and to a collaborative filtering recommendation technology, in particular to a social network-oriented multi-attribute collaborative filtering recommendation method. Background technique [0002] With the rapid development of information technology and the Internet, people have gradually entered the era of information overload from the era of information scarcity. In order to solve the problem of information overload, a powerful search engine came into being, allowing people to find what they want in a massive amount of information. Like search engines, recommendation systems can also help users find useful information, and users are no longer Passive web browsers, and gradually become active participants. [0003] In recent years, the recommendation system has been favored by more and more Internet giants and e-commerce companies, especially the development of personalized ...

AI Technical Summary

Problems solved by technology

[0005] Researchers have done a lot of research on the problem of sparsity, but they have not been able to effectively solve the impact of sparsity on the recommendation system

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