Online collaborative ordering method based on stochastic gradient descent

Abstract

The invention discloses an online collaborative ordering method. The method comprises the steps that a target function is built through a collaborative ordering method, and a stochastic gradient descent method is used for solving; an online collaborative ordering recommendation system is built for carrying out increment training, a recommendation list is updated in real time, and training and recommendation are achieved at the same time; a data set S is obtained and collaboratively divided into a large part and a small part; rating data of the small-proportion data set is used for building a user-product rating matrix X; a UVT model is obtained through decomposition; the online collaborative ordering method SGDRank and the small-proportion data set are used for off-line updating of a matrix U and a matrix V, and a UVT model is obtained; large-proportion data is used as an online sample to be added into a matrix X; the matrix U and the matrix V are trained on line, the UVT model is updated, the trained X matrix is obtained, and accordingly, online collaborative ordering for data is achieved. Ordering recommendation efficiency can be effectively improved by means of the method.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and in particular relates to an online collaborative sorting method based on stochastic gradient descent (the invention is referred to as SGDRank for short). Background technique [0002] Collaborative filtering technology is the core algorithm of the recommendation system, which mines the potential interests of users according to the similarity between users and the similarity between products. In previous studies, collaborative filtering models often recommend products by predicting user ratings. In recent years, many researchers have directly optimized the product recommendation list, transformed the recommendation problem into a ranking problem, and achieved good results. [0003] Collaborative ranking uses the method of ranking learning to solve the problem of collaborative filtering, trains the recommendation model according to the user's preference relationship for the product and direc...

AI Technical Summary

Problems solved by technology

[0004] (1) In the case of few samples, existing collaborative ranking algorithms cannot make effective recommendations;

[0005] (2) When a new sample arrives, the existing collaborative ranking algorithm cannot perform online training and update the recommendation results in real time;

[0006] (3) The training time of existing algorithms is often longer, and the recommendation effect needs to be improved

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