Personalized film recommendation method and system based on feature augmentation

Abstract

The invention discloses a personalized film recommendation method and system based on feature augmentation. The necessity and feasibility that the personalized recommendation system is transferred to a cloud calculation platform are discoursed by analyzing the problems brought by mass data to the existing personalized recommendation system, and by combining a mixed recommendation model adopting feature augmentation, the method comprises the steps that an original extremely-sparse user item rating matrix is filled for the first time through a collaborative filtering algorithm based on items to generate a pseudo two-dimensional table, then the pseudo two-dimensional table is further filled through a collaborative filtering algorithm based on the user, and lastly the defects of strict object attribute matching are avoided through conversion of quantitative knowledge and qualitative knowledge of a cloud module. According to the method, all rating data of the user is fully utilized, the calculation efficiency is greatly improved and calculation speed is greatly increased through parallel calculation of cloud calculation, therefore, the better experience is provided for the user, and the advantages of an enterprise in competition are achieved.

Description

technical field [0001] The invention belongs to the application field of collaborative recommendation on movies, and in particular relates to a feature-increasing-based personalized movie recommendation method and system. Background technique [0002] With the rapid development of Internet technology, mass entertainment has increasingly become an indispensable part of people's daily life. As a form of artistic expression, film is more and more accepted and loved by the public. [0003] With the surge in the number of moviegoers, people's demand for movies has also increased. For this reason, it has brought about a hot scene where hundreds of thousands of movies are released every year. Take an extreme case: If a movie playback client allows users to randomly order from it, and does not classify movies, it will not allow users to quickly find their favorite movies. In this case, the user probably feels that the movie playback system used is not humanized enough, which caus...

AI Technical Summary

Problems solved by technology

[0012] The purpose of the present invention is to provide a feature-increasing-based personalized movie recommendation method and system, aiming to solve the limitations of the collaborative recommendation algorithm in the field of movie recommendation due to the "data sparsity" and "cold start" of the algorithm itself. Issues leading to poor quality of collaborative recommendations

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