Collaborative filtering-based content recommendation system and method

Abstract

The invention discloses a collaborative filtering-based content recommendation system and method. The system comprises a data preprocessing module, an algorithm mixing module, and a result generation module, wherein the algorithm mixing module further comprises an algorithm selection unit, a weighting similarity-based collaborative recommendation algorithm unit, a balance score prediction mechanism-based collaborative recommendation algorithm unit, a score filling-based mixed recommendation algorithm unit, and a collaborative recommendation algorithm unit using score time characteristics; and the algorithm mixing module inputs preprocessed data to the weighting similarity-based collaborative recommendation algorithm unit, the balance score prediction mechanism-based collaborative recommendation algorithm unit, the score filling-based mixed recommendation algorithm unit, and the collaborative recommendation algorithm unit using the score time characteristics, and outputs an algorithm result to the result generation module. According to the system and the method, the sparsity problem and the concept drift problem in the recommendation system are better solved.

Description

technical field [0001] The present invention relates to recommendation algorithms and systems thereof, more specifically, to a content recommendation system and method based on collaborative filtering. Background technique [0002] With the popularity of Internet technology, people's lives are increasingly inseparable from the Internet, and more and more people choose to perform entertainment or shopping on the Internet. In the face of ever-expanding user needs and increasingly abundant resources, how to fully understand the needs of users and quickly find the resources they need for users has become a powerful means to attract users. Based on this demand, personalized recommendation technology has gradually received attention, and now it has entered a stage of mature development. [0003] Personalized recommendation technology is an important aspect of user behavior analysis technology. Simply put, it is a process of finding resources that may be of interest to users. In ...

AI Technical Summary

Problems solved by technology

[0006] However, in the process of development and application, the recommendation system is affected by various problems to varying degrees, especially the sparsity problem and the concept drift problem have become the most important problems affecting the quality of recommendation.

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