Dynamic graph sequence recommendation system sensitive to user interaction

Abstract

A dynamic graph sequence recommendation system sensitive to user interaction is realized through a method in the technical field of artificial intelligence. The whole system adopts a reinforcement learning framework, data input is scoring data of a user for commodities with timestamps and attribute data of the user, output of the system is a recommended commodity sequence generated by continuous multi-round recommendation, a recommendation result of each round is obtained after an intelligent agent observes a system environment subjected to dynamic graph modeling, and an optimal recommendation decision is made based on the state representation of a dynamic graph environment, the commodity representation, the real-time interest of the user in the commodities and the attribute information of the user. The operation process of the system is divided into five modules in sequence, an off-line training mode in reinforcement learning is adopted for training, a small-batch gradient descent method is used for optimizing parameters, an environment state is modeled by using a graph neural network and a self-attention mechanism, a recommendation strategy can be generated based on the real-time global environment state to obtain recommendation, and the system has strong real-time performance, high dynamic performance and expandability.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to a dynamic graph sequence recommendation system sensitive to user interaction. Background technique [0002] With the gradual deepening of social and economic informatization, problems such as information explosion and information overload are becoming more and more serious. Therefore, the way people obtain information is gradually changing from "people looking for information" to "information looking for people". As we all know, the recommendation system is an effective means to solve data overload. Accurate and effective recommendations can not only improve user experience and user stickiness, but also improve the efficiency of information transmission, which can directly or indirectly create more benefits. However, the user's interests and hobbies will change over time, and each interaction between the user and the recommendation system will be affected by its historical ...

AI Technical Summary

Problems solved by technology

However, on the one hand, these existing methods only consider the short-term benefits brought by single-step recommendation to users and the system in the recommendation process, which has great limitations; on the other hand, most of the existing methods are based on strong timing assumptions , adopting a sequence model or a static graph + sequence model to model the state of the environment, and this assumption is not applicable in all scenarios

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