A collaborative filtering recommendation method based on discrete multi-view hashing

Abstract

The invention discloses a collaborative filtering recommendation method based on discrete multi-view hashing, comprising the following steps: 1) constructing a multi-view anchor graph representation of data according to data under different views; 2) combining collaborative filtering and anchor graphs, Obtain the learning model; 3) Solve the obtained learning model to obtain the binary hash code corresponding to the user and the item; 4) Use the obtained hash code to perform the nearest neighbor search, calculate the preference degree of the specific user for the candidate item, and return the preference Several items with the highest degree are used as the recommendation results. The present invention integrates the data under different views, keeps the discrete characteristic of coding all the time when solving the problem, and improves the quality of the recommendation result. At the same time, hash coding is used to realize the fast search of similar users and improve the efficiency of calculation of recommendation results.

Description

technical field [0001] The invention relates to a personalized recommendation technology, in particular to a collaborative filtering recommendation method based on discrete multi-view hashing. Background technique [0002] The rapid development of the Internet industry has brought explosive growth in content. In order to help users obtain information effectively, personalized recommendation systems are playing an increasingly important role. Collaborative filtering technology is a kind of technology that has received wide attention in recommender systems. Different from the traditional recommendation based on content-based direct filtering analysis, collaborative filtering uses a large amount of user information to select users similar to the target user or items similar to the target item to finally recommend items that the current target user may be interested in. [0003] But in the real application environment, we can often obtain a lot of other information besides the...

AI Technical Summary

Problems solved by technology

Traditional collaborative filtering recommendation technology can only use user information in a single view, and needs to calculate the prediction score of user preference through the operation between high-dimensional vectors, which seriously affects the calculation and storage efficiency. The conventional solution Also caused a lot of information loss

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