Multi-relation collaborative filtering recommendation based on dynamic graph attention network

Abstract

The invention discloses a multi-relationship collaborative filtering recommendation method based on a dynamic graph attention network. The method comprises the following steps: S1, performing data acquisition and processing; S2, dividing a data set; S3, constructing a fusion model; and S4, model training and project recommendation. According to the invention, a recurrent neural network (RNN) is used for modeling behaviors of a user in a session, the current interest of the user is captured through RNN potential representation, the influence of friends related to the user is captured through agraph attention network, the influence of each friend is weighed by measuring the characteristics of movement along each side according to an attention mechanism, and the current user representation and the social friend representation are combined; the project relationship is obtained from the interaction data of the user and the project, the project relationship and the user dynamic social relationship are fused into the learning process of the user and the project interaction, the influence of multiple relationships on the user and the project interaction is learned, and the recommendationaccuracy is improved, so that the model can better model the user preference.

Description

technical field [0001] The invention belongs to the technical field of computer artificial intelligence, and in particular relates to a multi-relationship collaborative filtering recommendation method based on a dynamic graph attention network. Background technique [0002] At present, the recommendation system plays an important role in our life and work. E-commerce platforms and life-like network applications generally use recommendation systems to solve the problem of information overload. Among them, the online social community is an important part of today's online experience, and has become an indispensable part of many users' daily life. Therefore, the recommender systems of these platforms are crucial to focus on users’ superficial information and improve long-term user engagement. However, online communities bring additional challenges to the recommender system. Users’ interests are dynamic, and users are easily influenced by friends’ interests. For example, a us...

AI Technical Summary

Problems solved by technology

However, online communities pose additional challenges for recommender systems

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