Session-based recommendation: Learning multi-dimension interests via a multi-head attention graph neural network

In a recent study, it was shown that, with batch training of a graph neural network, it is possible to recommend suitable items for users. Although the method has obtained item embedding and considered the complex transitions between items, there is no multi-dimensional focus on the users' interests and preferences. In this paper, we propose a multi-head attention graph neural network (MAE-GNN) for session-based recommendation by combining a dual-gated graph neural network and multi-head attention mechanisms. MAE-GNN can select important node information and extract users' interests and preferences from multiple dimensions. Experimental evaluation has been conducted to show that, compared with the state-of-the-art methods, the proposed model has significant improvement in term of P@K and MRR@K for session-based recommendation.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

In this paper, a multi-head attention graph neural network (MAE-GNN) is proposed for session-based recommendation. The model combines a dual-gated graph neural network and multi-head attention mechanisms to extract users' interests and preferences from multiple dimensions, and experimental evaluation shows significant improvement compared to state-of-the-art methods.