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High-order attentive graph neural network for session-based recommendation.
- Source :
- Applied Intelligence; Nov2022, Vol. 52 Issue 14, p16975-16989, 15p
- Publication Year :
- 2022
-
Abstract
- Recommender systems are becoming a crucial part of several websites. The purpose of session-based recommendations is to predict the next item that users might click based on users' interaction behavior in a session. The latest research on session-based recommendation focuses on using graph neural networks to model transfer relationships between items. However, when the interaction of low-order relationships between adjacent items is insufficient, learning the high-order relationships between non-adjacent items becomes a challenge. Additionally, to distinguish the importance of nodes in the graph, different weights should be assigned to each edge. Therefore, we propose a novel high-order attentive graph neural network (HA-GNN) model for session-based recommendations. In the proposed method, first, we model sessions as graph-structured data. Then, we use the self-attention mechanism to capture the dependencies between items. Next, we use the soft-attention mechanism to learn high-order relationships in a graph. Finally, we update the embeddings of items using a simple fully connected layer. Experiments on two public e-commerce datasets show that HA-GNN has excellent performance. [ABSTRACT FROM AUTHOR]
- Subjects :
- RECOMMENDER systems
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Details
- Language :
- English
- ISSN :
- 0924669X
- Volume :
- 52
- Issue :
- 14
- Database :
- Complementary Index
- Journal :
- Applied Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 160112716
- Full Text :
- https://doi.org/10.1007/s10489-022-03170-7