User multi-interest and interest shift-based collaborative filtering recommendation algorithm

Abstract

The invention discloses a user multi-interest and interest shift-based collaborative filtering recommendation algorithm. The user multi-interest and interest shift-based collaborative filtering recommendation algorithm comprises the steps of 1) structuring a user-project attribute category correlation matrix; 2) according to the user-project attribute category correlation matrix, computing user similarity to predict the degree of preference of a user to unknown project attribute categories and further to recommend preference categories to the user; 3) classifying scoring matrixes according tothe recommended categories, and computing the project similarity under every category; 4) considering the interest shift of the user, computing the time weight and the degree of novelty of the projects to acquire a preliminary prediction score; 5) combining user-project attribute category preference to acquire a final prediction score. The user multi-interest and interest shift-based collaborativefiltering recommendation algorithm takes projects as the bridge between users and project attribute categories, acquires preference to the project attribute categories, well solves the problem of singleness of user interest models, takes interest shift of the users and the degree of novelty of the projects simultaneously into consideration and accordingly ensures more accurate final recommendation results.

Description

technical field [0001] The invention relates to the field of recommendation systems for data mining, in particular to a collaborative filtering recommendation algorithm based on multiple interests and interest changes of users. Background technique [0002] In recent years, with the rapid development of the Internet and electronic information technology, the web has become an important channel for people to obtain information. At the same time, data has exploded, and traditional network services such as catalogs and search engines can no longer meet people's personalized information needs. , the recommendation system came into being. At present, personalized recommendation technology is widely used in e-commerce, news websites, social networking websites, music and movie websites and other systems to enhance the attractiveness of websites and improve user experience. Collaborative filtering is currently the most widely used and most mature recommendation algorithm. Its esse...

AI Technical Summary

Problems solved by technology

However, traditional collaborative filtering is only suitable for single user interest recommendation, and for multiple interests, the recommendation accuracy will be affected due to different interests.

Moreover, traditional collaborative filtering does not take into account the problem of user interest changes and item novelty, and assigns the same weight to all movie ratings. In real life, user interests are constantly changing and they prefer new items.

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