A serialized recommendation method based on item association relationship

Abstract

The invention provides a serialized recommendation method based on an item association relationship, which obtains interaction data between a user and an item from a network end. The interactive datais used to construct a symbiotic relation graph of the article, and the symbiotic relation graph is represented by an association relation graph adjacency matrix. Convolution of the adjacency matrix of the association relation graph is performed to obtain the association characteristics of the object. The relevant features of the object are inputted into the recommendation model for training; Therecommendation model outputs the serialized recommendation. The method can mine the implicit relationship between objects in user's behavior and train it with serialized recommendation model to provide service for user's serialized recommendation, utilizes the interactive data between users and articles to mine the association relationship between articles, and the vectorized representation of theassociation relationship, intuitively and objectively show the association characteristics of each article, uses the European distance to analyze the associated articles, and carries ou the end-to-end training and serialization recommendation model to provide the end-to-end serialization recommendation services for users.

Description

technical field [0001] The present invention relates to the field of information recommendation, in particular, relates to a serialization recommendation method based on item correlation, especially based on the graph convolution theory, focusing on the correlation between items in serialization recommendation, and end-to-end training item correlation part and serialized recommendation method to provide users with the final serialized recommendation service. Background technique [0002] As an information filtering task, recommendation has been extended to many applications in the real world, such as product recommendation and video recommendation. Most of the current recommender systems are built under the assumption that user preferences are static. However, user preferences change dynamically over time. Therefore, there are currently many works focusing on how to exploit the serialized information of user-item interactions. [0003] According to the way of exploiting t...

AI Technical Summary

Problems solved by technology

At the same time, it is difficult for this RNN-like serialization recommendation method to capture this static item relevance

[0005] The current RNN-based serialization recommendation method does not take into account the correlation between items in the user's consumption sequence

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