Multivariate information-driven approximate fusion network recommendation propagation method

Abstract

The invention provides a multivariate information-driven approximate fusion network recommendation propagation method. A propagation algorithm is recommended based on an approximate fusion network, the problems of single information source and data initial stage recommendation quality of a traditional recommendation algorithm can be effectively improved, participants in a network recommendation system are divided into four entity classes and six relationships from three key steps of an approximate fusion network recommendation propagation algorithm, and a probability transfer matrix is determined according to different types of relationships among entities. According to the invention, various types of information such as recommended objects, projects, labels and attributes and relations thereof are effectively fused; the problem of data sparseness and the problem of data initial stage recommendation quality caused by a single information source are relieved, so that recommendation results are more diversified, recommendation accuracy is obviously improved, robustness and robustness are good, calculation complexity is moderate, overall implementation is easy, and the method can be rapidly popularized to network recommendation system application and is high in market practical value.

Description

technical field [0001] The invention relates to an approximate fusion network recommendation propagation method, in particular to an approximate fusion network recommendation propagation method driven by multiple information, and belongs to the technical field of network recommendation propagation methods. Background technique [0002] With the large-scale popularization and rapid development of the Internet, the transmission and release of various information is becoming more and more convenient and efficient, which makes the amount of information on the Internet grow at an unprecedented rate. Tens of thousands of recommended objects are continuously uploaded and accessed every day, which makes the data of the entire Internet increase day by day, and the amount of data is increasing by leaps and bounds. Human beings have already entered an era of severe information overload from an era of information scarcity. Facing massive amounts of data, information filtering and identif...

AI Technical Summary

Problems solved by technology

[0007] Aiming at the deficiencies of the existing technology, the present invention provides a multi-information-driven approximate fusion network recommendation propagation method, based on the approximate fusion network recommendation propagation algorithm, which can effectively improve the traditional recommendation algorithm with single information source and the quality of recommendation in the initial stage of data. , starting from the three key steps of the approximate fusion network recommendation propagation algorithm, the participants in the network recommendation system are divided into four types of entities and six types of connections, according to the different types of connections between entities, the probability transition matrix is ​​determined, and then based on the three Two hypothetical principles: first, the more meta-paths between two entities, the more similar the two entities are; second, the more similar two entities are, the greater their contribution to the similarity between entities connected to them; The more paths there are, the more similar two entities are, and a similarity propagation algorithm between entities is proposed.

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