Recommendation system data completion method based on convex optimization local low-rank matrix approximation

Abstract

The invention relates to a recommendation system data completion method based on convex optimization local low-rank matrix approximation. The method includes the steps of 1) constructing a recommendation system data matrix M according to the user's rating of a product in a recommendation system, and representing the data on unrated products by the user as 0 element in M; 2) selecting anchor points, dividing the recommendation system data matrix into a plurality of local matrices using a kernel smoothing method, the number of local matrices being the same as the number of the anchor points; and 3) solving a matrix completion model according to a convex optimization local low-rank matrix approximation algorithm, and completing the 0 element in the matrix M according to the matrix completion model to obtain a recommendation system data matrix X after completion. Compared with the prior art, the invention can finish the recommendation system matrix data completion while guaranteeing the operation speed and the accuracy.

Description

technical field [0001] The invention relates to the field of recommendation systems, in particular to a data completion method for recommendation systems based on convex optimization local low-rank matrix approximation. Background technique [0002] Currently, personalized recommendation services are widely used in reality. The main product of the personalized recommendation service is the recommendation system. The recommendation system is based on the user's past record information, including purchase records, browsing records, ratings, etc., to analyze and predict the user's preference for other products and tap potential consumer demand. [0003] The recommendation system not only has great academic value, but also is a research hotspot in the field of e-commerce. Many e-commerce systems recommend personalized information to users through recommendation systems. For example, 20%-30% of the annual sales revenue of the e-commerce website Amazon comes from recommendations. ...

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Problems solved by technology

[0007] However, with the rapid development of the Internet and e-commerce in recent years, the number of users and the number of items have become very large numbers, and these two sets of huge numbers combine to form a larger user-item rating matrix. However, because each The items that a user can access are limited, and only a small number of items that are rated by users can only account for a small number, so that the vast majority of the user-item rating matrix is ​​vacant, which in turn makes the user-item rating matrix have a high degree of sparseness. In this way, when the data recommendation system predicts a user's rating for an item, since the rating overlap between users is small, it is obviously not very accurate to predict a rating for a user's item through rating data of similar users

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