Movie recommendation method based on feedback of users' various behaviors

Abstract

The invention discloses a movie recommendation method based on feedback of users' various behaviors. The method includes the steps of s1, clustering movies, to be specific, subjecting movie information to feature selection to obtain keyword description of each movie; s2, calculating user similarity, to be specific, clustering users into a plurality of user sets by means of content clustering-behaviors based on the fuzzy theory, with each user having different membership degrees in the different user sets, performing modeling with movie description information and feedback data of users' various behaviors, calculating membership degrees of each user in the user sets, and calculating user similarity according to the membership degrees of the users in the different user sets; s3, generating recommendations, to be specific, generating different movie recommendation lists for the users according to the acquired user similarity. The method helps solve the problem of data sparsity, solve the problem of information loss occurring in traditional 'implicit-explicit' conversion and improve recommendation precision.

Description

technical field [0001] The invention belongs to the field of personalized recommendation, and in particular relates to a movie recommendation method based on feedback from various user behaviors. Background technique [0002] With the rapid development of the Internet, the data on the Internet is increasing exponentially. Traditional search algorithms can only present the same sorting results to all users, and cannot provide corresponding services for different users' hobbies. The explosion of information reduces the utilization rate of information, which is called information overload. Personalized recommendations, including personalized search, are considered to be one of the most effective tools currently available to address information overload. [0003] Recommendation algorithm is the core of personalized recommendation system. Recommendation algorithm can be divided into content-based recommendation algorithm, collaborative filtering recommendation algorithm, and kn...

AI Technical Summary

Problems solved by technology

[0004] However, the current mainstream collaborative filtering recommendation algorithm is mainly aimed at the rating prediction problem.

Since it is often difficult to obtain scoring data in reality, the implicit feedback data of various user behaviors is usually converted into scoring data in practical applications. This approach not only leads to low recommendation accuracy, but also has the problem of data sparsity.

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