User cold start recommendation algorithm based on collaborative filtering hybrid filling

Abstract

The invention discloses a user cold start recommendation algorithm based on collaborative filtering hybrid filling. The algorithm is carried out according to the following steps in sequence: calculating the similarity between articles based on a user-article scoring matrix; for each article, selecting the article with high similarity as the adjacent article of the article, predicting the score ofthe article according to the score information of the target user for the adjacent article, and filling the user-article score matrix with the score to obtain a primary filling score matrix; calculating the similarity between the users according to the primary filling score matrix; for each target user, selecting a user with high similarity with the target user as an adjacent user of the user, predicting scores of articles which are not scored by the target user according to the scoring information of the adjacent user, and filling the filled scoring matrix again; and according to the final scoring matrix, selecting the first N articles with the highest prediction scores to recommend to the target user.

Description

technical field [0001] The invention relates to the technical field of recommendation, in particular to a user cold-start recommendation algorithm based on collaborative filtering mixed filling that can improve recommendation accuracy. Background technique [0002] Recommender systems are one of the most effective ways to solve the problem of information overload by providing personalized recommendations. Collaborative Filtering (CF) is a popular recommendation technology, which is divided into item-based collaborative filtering (Item-based collaborative filtering, IBCF) and user-based collaborative filtering (User-based collaborative filtering, UBCF). [0003] Although the collaborative filtering algorithm has achieved success in personalized recommendation, due to the huge number of items in the system and the small number of items rated by new users, the cold start problem of new users is a classic problem widely concerned in collaborative filtering recommendation algorit...

AI Technical Summary

Problems solved by technology

The traditional collaborative filtering method calculates the user U 3 When similarity with other users, only U 2 and U 3 to me 1 , while ignoring the rating information of other users, resulting in a lot of rating information not being fully utilized

Therefore, the user cold start problem remains to be solved

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