面向会话的需求感知注意图神经网络推荐模型

doi:10.3778/j.issn.1002-8331.2211-0248

摘要/Abstract

摘要： 针对现有基于图的会话推荐方法忽略了反馈数据中由于用户行为不确定性引起的噪声影响，存在无法准确和有效地捕捉用户偏好的问题，提出一种面向会话的需求感知注意图神经网络推荐模型（DAAGNNSR）。将具有时序性的会话数据构建为图，通过引入图神经网络学习图上节点嵌入表示；将提取的项目特征使用需求感知聚合器线性聚合为用户潜在需求矩阵，以自动削弱噪声干扰，同时用低秩多头注意力网络将该矩阵与全部项目特征进行逐项兴趣交互生成需求增强的项目表征；联合独立位置编码进一步分析项目间顺序关联，并且将生成的独立位置嵌入与项目表征进行线性融合；经过预测层生成推荐列表。将所提模型在Diginetica、Tmall和Nowplaying三个公共数据集上进行训练和测试，实验结果表明，该模型的推荐精度在各指标上均优于其他基线模型，与基于图上下文自注意力机制模型（GCSAN）相比，Diginetica上NDCG@10提高了5.6%，Tmall上Recall@10提高了6.4%；与基于图神经网络的SRGNN相比，Tmall上Precision@10提高了5.0%，推荐性能显著提升。

Abstract: Aiming at the existing graph-based session recommendation method, which ignores the noise effect caused by the uncertainty of user behavior in the feedback data, and there is a problem that it cannot accurately and effectively capture user preferences, a demand aware attention graph neural network for session-based recommendation (DAAGNNSR) model is proposed. Firstly, the session data with time series is constructed as a graph, and the node embedding representation on the graph is learned by introducing the graph neural network. Secondly, the extracted project features are linearly aggregated into a user potential demand matrix using a demand aware aggregator to automatically attenuate noise interference, and at the same time, the low-rank multi-head attention network is used to interact with all item features item by item to generate a demand enhancing project representation. Again the joint independent position coding further analyzes the sequential association between the items, and the resulting independent position embedding is linearly fused with the project representation. Finally, a ranking recommendation list is generated by the prediction layer. The proposed model is trained and tested on three common datasets of Diginetica, Tmall and Nowplaying, and the experimental results show that the recommended accuracy of the model is better than other baseline models in all indexes, and compared with the graph context self-attention network for session-based recommendation (GCSAN), the NDCG@10 on Diginetica is improved by 5.6% and the Recall@10 on Tmall is increased by 6.4%. Compared with the SRGNN based on graph neural networks, the Precision@10 on Tmall is improved by 5.0%, and the recommended performance is significantly improved.

Key words: session sequence recommendations, graph neural networks, low-rank multi-head attention mechanism, demand aware aggregator, stand-alone position encoding

郑小丽, 王巍, 杜雨晅, 张闯. 面向会话的需求感知注意图神经网络推荐模型[J]. 计算机工程与应用, 2024, 60(7): 128-140.

ZHENG Xiaoli, WANG Wei, DU Yuxuan, ZHANG Chuang. Demand Aware Attention Graph Neural Network for Session-Base Recommendation[J]. Computer Engineering and Applications, 2024, 60(7): 128-140.

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