Sequence recommendation method and device and computer readable storage medium

Abstract

The invention discloses a sequence recommendation method and device and a computer readable storage medium. The method comprises the steps of combining a bidirectional graph formed by a user set-project set with a knowledge graph in advance and unifying the bidirectional graph and the knowledge graph into a mixed knowledge graph; inputting a historical interaction sequence of the to-be-recommendeduser and the mixed knowledge graph into a sequence recommendation model; the model comprises a knowledge graph embedding module, a graph attention network and a recurrent neural network. A knowledgegraph embedding module encodes all nodes of the mixed knowledge graph into vectors, and a graph attention network recursively updates the embedding of each node according to the embedding of each nodeand the embedding of adjacent nodes so as to capture a global user-project relationship and a project-project relationship; the recurrent neural network encodes the user interaction sequence items toobtain dynamic preferences of the user; and finally, determining recommendation sequence information of the to-be-recommended user according to the output of the model, thereby performing high-accuracy sequence recommendation based on a high-order dependency relationship between entities in the knowledge graph and local graph contexts.

Description

technical field [0001] The present application relates to the technical field of deep mining, in particular to a sequence recommendation method, device and computer-readable storage medium. Background technique [0002] In the era of information explosion, sequential recommendation systems are widely used in various fields such as e-commerce, social media, news portals, etc., to help users find content they are interested in from massive amounts of information. In these scenarios, users' interests are usually dynamic and constantly changing. It can be understood that accurately characterizing users' dynamic preferences and extracting items' collaborative signals is conducive to building an effective recommendation system. [0003] Traditional sequential recommender systems are built based on the Markov chain model, which assumes that the previous action or previous items are the basis for the next activity, and can successfully simulate short-term item switching for recomme...

AI Technical Summary

Problems solved by technology

However, this method cannot recommend information to users accurately and efficiently because it does not consider the high-order dependencies between entities in the knowledge graph and the local graph context.

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