Discrete particle swarm optimization based local community detection collaborative filtering recommendation method

Abstract

The present invention discloses a discrete particle swarm optimization based local community detection collaborative filtering recommendation method, mainly in order to solve the problem of low recommendation accuracy due to that the prior art has sparseness when obtaining similarity data among users. The method comprises the following steps: obtaining scoring information of users to the recommendation item, and indirectly generating a relationship network among the users by using the scoring data of the users to the to-be-recommended item; calculating similarity among the users, carrying out local community detection on the user relationship network through the similarity so as to obtain the user community with the densest local, and expanding the user community to obtain the local user community; dividing the user relationship network into a plurality of user communities, selecting k users with the largest similarity in the user communities to form a neighbor user group; and according to the neighbor user group, predicting the score of the items that are not evaluated by the target users, and recommending the item with the largest predictive score to the users. According to the method disclosed by the present invention, a better recommendation result can be obtained, and the method can be applied to recommend items that the user is interested in to the user.

Description

technical field [0001] The invention belongs to the field of computer technology, and further relates to a collaborative filtering recommendation method for local community detection, which can be used in a recommendation system for personalized services. Background technique [0002] In recent years, with the technological advancement 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. The biggest advantage of personalized recommendation i...

AI Technical Summary

Problems solved by technology

The shortcomings of this method are: it is not easy to obtain local topology information when dividing the network as a whole, and it does not take into account that the network scale in social systems is very large in practical applications, and it takes a long time to divide the network as a whole

Because this method is very sensitive to the position of the source node, different community structures will be obtained for different source nodes, which will lead to wrong results for node community division.

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