A multi-intent-aware recommendation algorithm based on interactive graph convolutional networks

In recent years, graph neural networks (GNNs) have been widely applied in recommender systems. However, existing recommendation algorithms based on GNNs still face challenges in node aggregation and feature extraction processes because they often lack the ability to capture the interactions between...

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Bibliographic Details
Published in:Complex & intelligent systems Vol. 10; no. 3; pp. 4493 - 4506
Main Authors: Zhang, Junsan, Gao, Hui, Xiao, Sen, Zhu, Jie, Wang, Jian
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.06.2024
Springer Nature B.V
Springer
Subjects:
Algorithms
Artificial neural networks
Attention mechanism
Collaborative filtering
Complexity
Computational Intelligence
Data Structures and Information Theory
Engineering
Feature extraction
Graph convolutional networks
Graph neural networks
Original Article
Recommender system
Recommender systems
Attention mechanism
Graph convolutional networks
Collaborative filtering
Recommender system
ISSN:2199-4536, 2198-6053
Online Access:Get full text
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Summary:In recent years, graph neural networks (GNNs) have been widely applied in recommender systems. However, existing recommendation algorithms based on GNNs still face challenges in node aggregation and feature extraction processes because they often lack the ability to capture the interactions between users and items, as well as users’ multiple intentions. This hinders accurate understanding of users’ needs. To address the aforementioned issues, we propose a recommendation model called multi-intent-aware interactive graph convolutional network (Multi-IAIGCN). This model is capable of integrating multiple user intents and adopts an interactive convolution approach to better capture the information on the interaction between users and items. First, before the interaction between users and items begins, user intents are divided and mapped into a graph. Next, interactive convolutions are applied to the user and item trees. Finally, by aggregating different features of user intents, predictions of user preferences are made. Extensive experiments on three publicly available datasets demonstrate that Multi-IAIGCN outperforms existing state-of-the-art methods or can achieve results comparable to those of existing state-of-the-art methods in terms of recall and NDCG, thus verifying the effectiveness of Multi-IAIGCN.
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ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-024-01366-7