Personalized recommendation method based on combination of content and collaborative filtering

Abstract

The invention discloses a personalized recommendation method based on combination of content and collaborative filtering. The method is divided into a training stage and a personalized recommendationstage, wherein in the training stage, an existing user-new article pre-recommendation matrix is formed by acquiring data information, and in the personalized recommendation stage, article recommendation for a new user and further recommendation after scoring by the user are completed. Compared with the traditional personalized recommendation method, the method has the advantages that the problem of cold start can be solved, newly added articles are predicted and then recommended to the users, collaborative filtering recommendation is used after pre-recommendation, and diversity and accuracy ofrecommended articles are improved. Further, virtual users are generated with a clustering algorithm, matrix dimensions are reduced greatly, and computation complexity is reduced.

Description

technical field [0001] The invention relates to the technical field of personalized recommendation of big data, in particular to a personalized recommendation method based on the combination of content and collaborative filtering. Background technique [0002] In the information age, information has also become a resource. The rapid development of the information age has accelerated the progress of the whole world and the pace of people's lives, and the effectiveness of time utilization has become the key. And contrary to this, the explosive amount of data is an urgent problem to be solved. In this context, the rationality of the emergence and development of the recommendation system is well known. It is the goal of the recommendation system to select items for the user's preferences in a short period of time. [0003] The existing content-based and collaborative filtering recommendation algorithms have their own defects, especially the cold start problem of collaborative...

AI Technical Summary

Problems solved by technology

[0003] The existing content-based and collaborative filtering recommendation algorithms have their own defects, especially the cold start problem of collaborative filtering and the singleness of content-based recommendation, which cannot make these two recommendation systems achieve the expected recommendation effect.

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