Multi-relational collaborative filtering algorithm based on graph neural network

Abstract

The present invention provides a multi-relationship collaborative filtering algorithm based on graph neural network, comprising: step 1, processing the historical interaction data between users and items, and extracting all user sequences U therefrom. U and item sequence S I ; Step 2, separate each user sequence S U and each item sequence S I Constructed as user relationship graph G respectively U =(V U ,E U ) and the item relation graph G I =(V I ,E I ). The multi-relationship collaborative filtering algorithm based on the graph neural network provided by the present invention simultaneously models the item relationship and the user relationship by constructing the item relationship diagram and the user relationship diagram from the interaction data between the user and the item, and integrates the multi-relationship into the user and the user relationship. In the learning process of item interaction, the influence of multi-relationship on user-item interaction is learned, so that the model can better model user preferences.

Description

technical field [0001] The invention relates to the technical field of information processing, in particular to a multi-relationship collaborative filtering algorithm based on a graph neural network. Background technique [0002] Collaborative filtering is one of the most widely used recommendation algorithms in the industry, which can effectively solve the "information overload" problem caused by massive data. Collaborative filtering can learn user preferences based on the user's historical interaction data with items (such as ratings, clicks), thereby generating new recommended content for users. A good recommendation algorithm can not only help users find interesting content and improve user experience, but also increase traffic for merchants and create huge profits. [0003] However, traditional collaborative filtering models, such as matrix factorization, neural collaborative filtering, etc., often only consider the relationship between users and items when modeling us...

AI Technical Summary

Problems solved by technology

[0003] However, traditional collaborative filtering models, such as matrix factorization and neural collaborative filtering, often only consider the relationship between users and items when modeling user preferences, while ignoring the relationship between items and users. , so that it is not enough to fully learn the user's preference from the historical interaction between the user and the item. The interaction between the user and the item is usually affected by two factors: 1. The user's historical preference, the interaction between the items that the user has interacted with in the past Relationships can directly affect a user's interest in a new item. Existing collaborative filtering algorithms that consider item relationships do not take into account the important role of user roles in the interaction between users and items; 2. User's social relationship, that is, user The relationship between items, a user's preference is often influenced by his social friends, which indirectly affects the user's decision-making behavior, and the existing recommendation models that consider user relationships also do not take into account the complex relationship between items, and need Clear social relationship data is often difficult to obtain in reality. In addition, traditional recommendation models do not consider that the relationship between different historical items (users) contributes differently to the model's prediction of user preferences.

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