Session recommendation method based on space-time sequence diagram convolutional network

Abstract

The invention discloses a session recommendation method based on a space-time sequence diagram convolutional network. The method comprises the following steps: S1, modeling all session sequences intoa directed session graph; S2, constructing a global graph by taking common commodities in the session as links; S3, embedding an ARMA filter into a gated graph neural network, extracting a topologicalgraph signal which changes over time from the graph model, and obtaining a feature vector of each node involved in the session graph; S4, obtaining global preference information from historical sessions of the user by adopting an attention mechanism; S5, obtaining local preference information of the user from the last session clicked by the user, and obtaining final preference information of theuser in combination with the global preference information; S6, predicting the probability of possible occurrence of the next clicked commodity in each session, and giving a Top-K recommended commodity. According to the method, rich context relationships of clicked commodities can be captured from the global graph, global and local preferences of the user are accurately learned, the time attenuation effect of historical preferences of the user on current preferences is effectively evaluated, and accurate commodity prediction is provided.

Description

technical field [0001] The invention relates to the technical field of Internet services, in particular to a session recommendation method based on a time-space sequence graph convolutional network. Background technique [0002] With the rapid popularization of Internet shopping, the overload of online information is also an inevitable trend, so how users can obtain effective information from massive data has become a top priority. The recommendation system can provide personalized recommendations for different users, so that each user can obtain the information they want from the limited and diverse information screened by the recommendation system, and the session-based recommendation, as a branch of the recommendation field, can real-time Recommend products of potential interest to users, aiming to help online systems provide accurate and personalized recommendation services. At present, most existing session-based recommendation systems usually only model sessions as se...

AI Technical Summary

Problems solved by technology

But this may not be the case in real-world transaction data, because the user may just randomly put some of his / her favorite items into the shopping cart, so the model prediction results are not very accurate

[0005] (2) Although the recurrent neural network (RNN) has made significant progress compared with the traditional conversational recommendation system, most existing RNN-based models do not reveal the global information of frequent click patterns, and most methods consider modeling in Inherent strengths in terms of sequential dependencies also do not take into account changes in user interests over time

However, this operation distracts the distribution of attention, which leads to a lack of local dependence on neighboring items and limits its ability to learn contextual representations of items

Images