Recommendation method based on Mask mechanism and hierarchical attention mechanism

Abstract

The invention relates to a recommendation method based on a Mask mechanism and a hierarchical attention mechanism. The recommendation method comprises the steps of obtaining a node data set U and a project data set I; inputting U and I into a context description layer to obtain sequences Su and Si, processing to obtain a node vector and a project vector, fusing the node vector and the project vector to obtain an embedded vector of the ith node u in I, and forming a vector set Z by the embedded vectors of all nodes; calculating a k-head attention coefficient of an L-order neighbor node v closely related to the ith node u, calculating k-head attention of the node u according to an embedded vector of the v, aggregating to obtain an aggregated attention vector of the ith node u, and splicing and linearizing the aggregated attention vector; obtaining a recommendation vector of the ith node u, and forming a vector set Z' by the recommendation vectors of all the nodes; and obtaining a projectrecommendation list of the target node according to Z and / or Z '. According to the method, the recommendation accuracy is improved, the flexibility of the network is improved, the cold start problemis solved, the model is simpler, and the time consumed for recommendation is shorter.

Description

technical field [0001] The invention relates to the field of interest recommendation, in particular to a recommendation method based on a Mask mechanism and a hierarchical attention mechanism. Background technique [0002] With the accelerated pace of people's daily life, quickly obtaining useful information in a practical way can save a lot of time, and recommender systems play a vital role in information filtering. Data sparsity, cold start and inference problems have always been the three major problems that affect the quality of recommendation. Collaborative filtering is the mainstream traditional recommendation algorithm, but due to the sparsity of data, the quality of recommendation is seriously affected. Collaborative filtering recommendation methods based on neural networks (such as CNN, RNN, etc.) alleviate the problem of data sparsity. In addition, social network-based methods can effectively provide recommendations for new users, new items and new stores, and hav...

AI Technical Summary

Problems solved by technology

Data sparsity, cold start and inference problems have always been the three major problems that affect the quality of recommendations. Collaborative filtering is the mainstream traditional recommendation algorithm, but due to the sparsity of data, the quality of recommendations is seriously affected.

However, whether it is a graph convolutional neural network or a graph attention network, the nodes in the social network are aggregated in the same domain, and the training time is longer when the network scale is large.

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