Session-based project recommendation method, device and apparatus and storage medium

Abstract

The invention discloses a session-based project recommendation method, device and apparatus and a computer readable storage medium. In the scheme of the invention, the method comprises the following steps of: constructing a directed session structure diagram through a historical session sequence of a user; based on the session structure diagram, enabling a graph neural network to capture conversion between adjacent projects; generating implicit state vectors of all nodes in the graph; establishing long-distance dependence by using a self-attention mechanism, and finally predicting the probability of next click by taking the linear combination of the global preference and the current local preference of a user as the hidden vector of a current session. The method utilizes the complementaryadvantages of the self-attention network and a graph neural network to realize the accurate recommendation of a project.

Description

technical field [0001] The present invention relates to the technical field of item recommendation, and more specifically, relates to a session-based item recommendation method, device, equipment, and computer-readable storage medium. Background technique [0002] With the continuous development of mobile Internet technology, the amount of information on the network is rapidly expanding and increasing exponentially, and the problems of information overload and information wandering on the network are becoming more and more serious. In order to provide users with satisfactory information and services, recommender systems emerged as the times require, and have become a research field that many researchers pay attention to. Many existing recommendation systems are based on the user's historical interaction records. However, in many application scenarios, the user's identity is unknown. In an ongoing session, there are only the user's historical records. To solve this problem, ...

AI Technical Summary

Problems solved by technology

However, these existing methods usually model unidirectional transitions between consecutive items, ignoring complex transitions between entire conversational sequences.

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