Multi-Behavior Enhanced Heterogeneous Graph Convolutional Networks Recommendation Algorithm based on Feature-Interaction

Graph convolution neural networks have shown powerful ability in recommendation, thanks to extracting the user-item collaboration signal from users' historical interaction information. However, many existing studies often learn the final embedded representation of items and users through IDs of...

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Bibliographic Details
Published in:Applied artificial intelligence Vol. 37; no. 1
Main Authors: Li, Yang, Zhao, Fangtao, Chen, Zheng, Fu, Yingxun, Ma, Li
Format: Journal Article
Language:English
Published: Philadelphia Taylor & Francis 31.12.2023
Taylor & Francis Ltd
Taylor & Francis Group
Subjects:
Algorithms
Artificial neural networks
Embedding
Graph neural networks
Neural networks
Representations
ISSN:0883-9514, 1087-6545
Online Access:Get full text
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Summary:Graph convolution neural networks have shown powerful ability in recommendation, thanks to extracting the user-item collaboration signal from users' historical interaction information. However, many existing studies often learn the final embedded representation of items and users through IDs of user and item, which cannot make well explanation why user choose the item. By making good use of item's attribute, the networks will gain better interpretability. In this article, we construct a heterogeneous tripartite graph consisting of user-item-feature, and propose the attention interaction graph convolutional neural network recommendation algorithm (ATGCN). We embed multi-feature fusion of users and items into the user feature interaction layer by using multi-head-attention, which explore the user's potential preference to update the user's embedded representation. Through the neighborhood aggregation of graph convolution, the feature neighbors' aggregation of items is constructed to achieve higher-order feature fusions, and the neighborhood aggregation of users and items is carried out on the historical interaction information. Then, the final embedding vector representations of user and item are obtained after many iterations. We verify the effectiveness of our proposed method on three publicly available datasets and ATGCN has improved 1.59%, 2.03%, and 1.27% in normalized discounted cumulative gain (NDCG), Precision and Recall, respectively.
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ISSN:0883-9514
1087-6545
DOI:10.1080/08839514.2023.2201144