Multi-behavior migration recommendation method based on deep learning

Abstract

The invention discloses a multi-behavior migration recommendation method based on deep learning, and the method comprises the steps: firstly obtaining and processing a plurality of implicit feedback data sets of a user; constructing a base network Gb and a plurality of behavior networks G (k), and learning low-dimensional embedded representations of users and article nodes in each network by usinga network representation learning method; then, based on different influence of multiple implicit behavior feedbacks of the user on user preference modeling, using an attention mechanism for automatically learning the weight of each behavior, and acquiring fused low-dimensional embedded representation of the user and the object finally, naturally splicing and sending low-dimensional embedding vectors of the user and the articles and to a full-connection embedding layer, adopting and feeding back a preference learning method based on a deep neural network to a feedforward neural network witha hidden layer, wherein the preference of the user for articles is learned on an output layer. The method can better capture the preference of the user and realize personalized recommendation, and has the advantages of high recommendation accuracy, strong generalization ability, easiness in realization and the like.

Description

technical field [0001] The invention belongs to the technical field of data mining and recommendation, and in particular relates to a multi-behavior migration recommendation method based on deep learning. Background technique [0002] With the explosive growth of information and content on the Internet, human society has entered an era of "information overload". It is difficult for people to quickly and efficiently find the content or items they need, so the recommendation system came into being. The core of the recommendation system is the recommendation algorithm, which helps users find interesting items from massive data by mining the relationship between users and items, and generates a personalized recommendation list. The traditional recommendation methods mainly include collaborative filtering method, content-based recommendation method and hybrid recommendation method, in which the collaborative filtering method uses the user's historical behavior records to recomme...

AI Technical Summary

Problems solved by technology

[0004] At present, most recommendation systems focus on the analysis and mining of explicit feedback information, and capture the shallow relationship between users and items through matrix decomposition and other methods, but cannot dig deep into the deep features of users and items. Lack of information will lead to data sparsity problems, resulting in poor recommendation effect

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