计算机科学
会话(web分析)
依赖关系(UML)
依赖关系图
过渡(遗传学)
图形
序列(生物学)
利用
序列学习
人工神经网络
人工智能
数据挖掘
机器学习
理论计算机科学
万维网
基因
生物
化学
生物化学
遗传学
计算机安全
作者
Wei Guo,Shoujin Wang,Wenpeng Lü,Hao Wu,Qian Zhang,Zhufeng Shao
标识
DOI:10.1109/dsaa53316.2021.9564224
摘要
Session-based recommendations (SBR) play an important role in many real-world applications, such as e-commerce and media streaming. To perform accurate session-based recommendations, it is crucial to capture both sequential dependencies over a sequence of adjacent items and complex item transitions over a set of items within sessions. Note that item transitions are not necessarily dependent on sequential dependencies, e.g., the transition from one item to the other distant item in a session is often not sequential. However, almost all the existing session-based recommender systems (SBRS) fail to consider both kinds of information, which leads to their limited performance improvement. Aiming at this deficiency, we propose a novel sequential dependency enhanced graph neural network (SDE-GNN) to capture both sequential dependencies and item transition relations over items within sessions for more accurate next-item recommendations. Specifically, we first devise a sequential dependency learning module to capture the sequential dependencies over a sequence of adjacent items in each session. Then, we propose an item transition learning module to capture complex transitions between items. In the module, a novel residual gate and a specialized attention mechanism are integrated into gate-GNN to build an attention augmented GNN, called AU-GNN. Finally, we devise a gated fusion component to combine the learned sequential dependencies and item transitions together in preparation for the subsequent next-item recommendations. Exhaustive experiments on two public real-world data sets demonstrate the superiority of SDE-GNN over the state-of-the-art methods.
科研通智能强力驱动
Strongly Powered by AbleSci AI