Personalized recommendation method based on bilateral diffusion of bipartite network

Abstract

The invention discloses a personalized recommendation method based on bilateral diffusion of a bipartite network, which mainly solves the conflicts among several main arithmetic performance indexes including accuracy, diversity and novelty of a recommendation list with respect to personalized recommendation of Top-N. The method comprises the following steps: (1) constructing a user-article associated bipartite graph network, wherein it is supposed that each target user node has certain allocable resource, which can be allocated to other node objects according to a preset allocation mechanism; (2) establishing an article-article and user-user second-order correlation matrix; (3) implementing a bilateral diffusion process to obtain a user-article interest matrix; and (4) giving a recommendation list of each user with the length N to complete the recommendation of Top-N. Based on the thought of network communication, the effect of the method is obviously better than that of a classical collaborative filtering method, the long tail mining concerned by a personalized recommendation system is better realized, and the method can be used for solving the Top-N problem of personalized recommendation.

Description

technical field [0001] The present invention belongs to the field of recommendation system, combines the method of network dissemination with the personalized recommendation system that has attracted much attention at present, and is specifically a personalized recommendation method based on the two-way diffusion of the initial resources of the bipartite network, which can be used to solve the problem of personalized Top-N recommendation. Background technique [0002] With the rapid development of information technology and the advent of the Internet age, people's lives have also undergone profound and huge changes. Network life has become an indispensable part, such as online shopping malls, online theaters, online bookstores, etc., which have brought great convenience to people's lives, but at the same time, information overload makes people feel overwhelmed and tired In the information, I am struggling to find useful information for myself. For example, there are million...

AI Technical Summary

Problems solved by technology

The heat conduction algorithm (Heats) based on the bipartite network is a recommendation algorithm proposed by simulating the process of initial resource allocation. Due to the fact that the algorithm suppresses the influence of popular items to a large extent, the recommendation results show too many long-tail items. Therefore, there is a good novelty and poor accuracy, which means that the recommendation result cannot predict the user's needs very well. Even if the product coverage is good, it cannot be directly used as a recommendation result to the user.

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