Method and system for solving cold start problem in collaborative filtering technology

Abstract

The invention belongs to the technical field of personalized recommendation, and particularly relates to a method and system for solving a cold start problem in a collaborative filtering technology. The method for solving the cold start problem in the collaborative filtering technology comprises the steps that a data set is selected; an initial user or project clustering model is built through an optimized genetic algorithm; clustering is conducted on the basis of the initial user or project clustering model, and a user or project clustering model is obtained; entropy values of new users or new projects to all kinds of clusters in the clustering model are calculated, and the new users or the new projects are subjected to class cluster dividing; the new users or the new projects are recommended. The invention further provides a system for solving the cold start problem in the collaborative filtering technology. The system comprises a selection module, an initial model building module, a clustering module, a class cluster dividing module and a recommendation generation module. Accordingly, an improved genetic algorithm is utilized for conducting K-Means clustering, the initial user or project clustering model is generated, and recommendation is generated for the new users or the new projects.

Description

technical field [0001] The invention belongs to the technical field of personalized recommendation, and in particular relates to a method and system for solving the cold start problem in collaborative filtering recommendation technology. Background technique [0002] Information overload is one of the most serious problems in the big data environment, and recommender systems, as an effective way to alleviate this problem, have attracted more and more attention from industry and academia. As one of the most widely used recommendation technologies, collaborative filtering recommendation technology has achieved a lot of achievements in both theoretical and applied research, but there is a cold start problem, which affects the recommendation quality of collaborative filtering recommendation technology. [0003] The cold start problem includes the new user cold start problem and the new project cold start problem. The traditional collaborative filtering recommendation technology...

AI Technical Summary

Problems solved by technology

However, the traditional K-Means clustering algorithm is random in the selection of the initial user center point, which is not only inefficient but also produces a local optimal situation, which affects the final clustering division and thus affects the entire recommendation quality

The selection crossover probability and mutation probability in the traditional genetic algorithm are fixed, that is, they remain unchanged throughout the evolution process, but the difference between the initial stage and the end stage of the population is very large. Larger crossover and mutation probabilities, thereby increasing the diversity of the population, and at the end of population evolution, the differences between individuals are relatively small, so it is necessary to adjust the crossover probability and mutation probability in time

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