Network community based collaborative filtering recommendation method

Abstract

The invention discloses a network community based collaborative filtering recommendation method which mainly solves the problem that a recommendation accuracy rate is low in the prior art due to sparsity in acquisition of similarity data of users. The network community based collaborative filtering recommendation method includes: acquiring rating information for recommended items from users and generating a user relation network among the users indirectly through the rating information for the recommended items from the users; computing similarity among the users; partitioning the user relation network into multiple user communities via similarity-based community detection; selecting k users, with the largest similarity, from a local community of the user to form a neighbor user set, and predictively rating items not rated by target users according to the neighbor user set; recommending the item rated with the highest prediction value to the user. From a result of a simulation experiment, the network community based collaborative filtering recommendation method can obtain a better recommendation result as compared with a conventional collaborative filtering recommendation method, and can be used for recommending the items the users interested in to the users.

Description

technical field [0001] The invention relates to the technical field of social networks, in particular to a network community-based collaborative filtering recommendation method. Background technique [0002] In recent years, with the emergence and popularization of computer network technology in real life, personalized service has become a new information service model, and recommendation technology is an important part of personalized service. Personalized recommendation refers to the technology that collects, filters, and classifies user information according to the user's hobbies, finds items or information that the user is interested in, and recommends it to the user. The application of personalized recommendation is increasingly widespread, and the application fields involve e-commerce, web pages, movies, books, music and many other aspects. For example, some e-commerce websites collect and analyze the user’s purchase history, predict the products that the user may be ...

AI Technical Summary

Problems solved by technology

However, this method usually has a clear limit on the number of divided communities

The method based on hierarchical clustering is to establish predefined heuristic rules through

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