Collaborative filtering recommendation method for integrating time contextual information

Abstract

The invention discloses a collaborative filtering recommendation method for integrating time contextual information, which is used for integrating the time contextual information on the basis of an original item-based collaborative filtering recommendation algorithm and an original user-based collaborative filtering recommendation algorithm and combining the original item-based collaborative filtering recommendation algorithm and the original user-based collaborative filtering recommendation algorithm into a uniform algorithm. The collaborative filtering recommendation method comprises the steps of for the user-based collaborative filtering recommendation algorithm, firstly, integrating a time attenuation function in a user similarity calculation stage; then, clustering items, and training interest attenuation factors of a user on an article category; finally, integrating the time attenuation function in a rating prediction stage, wherein for the item-based collaborative filtering recommendation algorithm, the process is similar to the process of the user-based collaborative filtering recommendation algorithm, and the two algorithms can be finally combined into the uniform algorithm. According to the collaborative filtering recommendation method disclosed by the invention, the time attenuation function is introduced in both the similarity computation stage and the rating prediction stage, different time attenuation factors are used for different types of items by different users, and thus the prediction accuracy can be effectively increased.

Description

technical field [0001] The invention relates to a personalized recommendation system technology, in particular to a collaborative filtering recommendation method incorporating temporal context information. Background technique [0002] Recommender systems emerged to solve the problem of information overload, and are widely used in e-commerce, movies, videos, music, design networks, reading, location-based services, personalized emails, and advertisements. By analyzing a large number of user behavior logs, the recommendation system can display different personalized pages to different users, improving the click-through rate and conversion rate of the website. [0003] Collaborative Filtering (Collaborative Filtering) algorithm is the earliest algorithm proposed in the field of recommendation system, which has been deeply researched and widely used in academia and industry. Collaborative filtering algorithms are divided into two categories: user-based collaborative filtering ...

AI Technical Summary

Problems solved by technology

However, the accuracy of existing collaborative filtering algorithms is poor

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