Recommendation method based on singular value decomposition and classifier combination

Abstract

The invention discloses a recommendation method based on singular value decomposition and classifier combination. The recommendation method comprises the steps of computing average score and probability distribution of an item through data preprocessing; training a singular value decomposition model through a stochastic gradient descent method, computing an entropy set of a scored item set of the user in item classification through a computing method of entropy, and determining an uncertainty critical value of the item; and comparing and predicting uncertainty and critical value of the item to determine whether to use a classifier, and recommending N items with highest scores in all non-scored items of the user through a Top-N method. According to the method, individual recommendation is produced on the basis of analysis of historical score data of the user; predicting score of a designated item i is acquired through a singular value decomposition algorithm, information entropy of the item for each user is calculated so as to determine whether to classify, and final prediction score of the item is acquired through the classifier, so that the accuracy of the recommendation method is improved.

Description

technical field [0001] The invention belongs to the technical field of recommendation algorithms, in particular to a method for fusion recommendation based on singular value decomposition and classifiers. Background technique [0002] Whether it is in the field of e-commerce or online video sites such as music and movies, information overload is one of the problems people face in the world today, and the recommendation system is the way to solve this problem. It mines massive data, analyzes users' historical browsing records, and scores historical items to obtain users' preferences in a certain field, thereby realizing personalized recommendations. [0003] Among the commonly used recommendation algorithms, there are currently three categories, namely, content-based recommendation algorithms, collaborative filtering algorithms, and hybrid recommendation algorithms. The content-based recommendation algorithm performs feature extraction, modeling and comparison on the user's ...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the embodiment of the present invention is to provide a method based on singular value decomposition and classifier fusion recommendation, which aims to solve the problems of poor performance of new users, low accuracy of system recommendation and complex attributes existing in existing recommendation algorithms

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