融合自注意力和图卷积的多视图群组推荐

doi:10.3778/j.issn.1002-8331.2304-0035

摘要/Abstract

摘要： 为了解决大多数现有的群组推荐仅仅从群组和用户的单一交互中学习群组表示，以及固定融合策略难以动态调整权重的问题。提出了一种融合自注意力和图卷积的多视图群组推荐模型（MVGR），设计了成员级、项目级和组级三个不同的视图，来捕捉群组、用户和项目三者之间的高阶交互信息，缓解数据稀疏问题，增强群组表示建模过程；对于项目级视图，利用基于二分图的图卷积神经网络来学习群组偏好向量以及项目嵌入；进一步提出了自适应融合组件来动态调整不同视图权重，得到最终的群组偏好向量。在两个真实数据集上的实验结果表明，与基线模型相比，MVGR模型的命中率（HR）和归一化折损累计增益（NDCG）在Mafengwo数据集上平均提高了8.89个百分点和1.56个百分点，在CAMRa2011数据集上平均提高了2.79个百分点和2.7个百分点。

关键词: 群组推荐, 自注意力机制, 图卷积神经网络, 自适应融合

Abstract: In order to solve the problem that most existing group recommendations only learn group representation from a single interaction between the group and the user, and that the fixed fusion strategy is difficult to dynamically adjust the weight. A multi-view group recommendation model (MVGR) is proposed, which integrates self-attention and graph convolution. Three different views, member level, item level and group level, are designed to capture high-level collaborative information among groups, users and items, alleviate the problem of data sparsity, and enhance group representation modeling. For item level views, the graph convolution neural network based on dichotomous graph is used to learn group preference vector and item embedding. MVGR further proposes an adaptive fusion component to dynamically adjust different view weights to get the final group preference vector. Experimental results on two real dataset show that the hit ratio (HR) and normalized discounted cumulative gain (NDCG) of the MVGR model are improved by an average of 8.89?percentage points and 1.56 percentage points on the Mafengwo dataset, and by an average of 2.79 percentage points and 2.7 percentage points on the CAMRa2011 dataset compared to the baseline model.

Key words: group recommendation, self-attention mechanism, graph convolution neural network, adaptive fusion

王永贵, 王芯茹. 融合自注意力和图卷积的多视图群组推荐[J]. 计算机工程与应用, 2024, 60(8): 287-295.

WANG Yonggui, WANG Xinru. Multi-View Group Recommendation Integrating Self-Attention and Graph Convolution[J]. Computer Engineering and Applications, 2024, 60(8): 287-295.

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