Asymmetrical weighing similarity based collaborative filtering recommendation method and system

Abstract

The invention discloses an asymmetrical weighing similarity based collaborative filtering recommendation method and system. The method includes: determining a user similarity asymmetrical weighing factor according to the proportion of user common scoring items; calculating the similarity among users having the common scoring items through the cosine similarity measurement method and the mean square error measurement method according to the user similarity asymmetrical weighing factor; performing fitting calculation on an original similarity matrix of a user through the matrix decomposition gradient descent method to obtain users who do not have the common scoring items; selecting K users having the maximum similarity as neighbors of a target user according to the similarity among the users having the common scoring items or not having the common scoring items, and predicting scores of user for items which have not being scored according to data of the k neighbors; and generating recommendation items of the target user. The method is accurate in calculation of user similarity and is high in recommendation quality, and can be widely applied to the technical field of recommendation.

Description

technical field [0001] The invention relates to the technical field of recommendation, in particular to a collaborative filtering recommendation method and system based on asymmetric weighted similarity. Background technique [0002] The rapid development of Internet technology has brought us into the era of information explosion. The simultaneous presentation of massive information not only makes it difficult for users to find the content they are interested in, but also makes a large amount of little-known information become "dark information" in the network. , which cannot be obtained by ordinary users. The recommendation system establishes a binary relationship between users and items (such as products, movies, music, programs, etc.), uses user history selection information or similarity relationships to mine potential interests of users, and then makes recommendations. [0003] Collaborative filtering is the most commonly used method and technology in current recommend...

AI Technical Summary

Problems solved by technology

[0005] 1) It does not consider the proportion of common rated items between users in all rated items, which leads to an error of high similarity between two users when there are few common rated items and the scores are similar As a result (although the scores of the two in other items differ greatly), it affects the accuracy of user similarity calculation;

[0006] 2) Assume that the mutual similarity between users is equal, but this assumption may not be true in some cases, resulting in misjudgment of the user's similarity;

[0007] 3) The problem of data sparsity and cold start is not considered, and the recommendation quality when data is sparse or cold start cannot be guaranteed

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