Affiliations: [a] School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, China
| [b] State Key Laboratory of Communication Content Cognition, Harbin Institute of Technology, Harbin, China
Correspondence:
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Corresponding author. Shaohui Liu, State Key Laboratory of Communication Content Cognition, Harbin Institute of Technology, Harbin 150000, China. E-mail: shliu@hit.edu.cn.
Abstract: Session-based recommendation is an overwhelming task owing to the inherent ambiguity in anonymous behaviors. Graph convolutional neural networks are receiving wide attention for session-based recommendation research for the sake of their ability to capture the complex transitions of interactions between sessions. Recent research on session-based recommendations mainly focuses on sequential patterns by utilizing graph neural networks. However, it is undeniable that proposed methods are still difficult to capture higher-order interactions between contextual interactions in the same session and has room for improvement. To solve it, we propose a new method based on graph attention mechanism and target oriented items to effectively propagate information, HOGAN for brevity. Higher-order graph attention networks are used to select the importance of different neighborhoods in the graph that consists of a sequence of user actions for recommendation applications. The complementarity between high-order networks is adopted to aggregate and propagate useful signals from the long distant neighbors to solve the long-range dependency capturing problem. Experimental results consistently display that HOGAN has a significantly improvement to 71.53% on precision for the Yoochoose1_64 dataset and enhances the property of the session-based recommendation task.
