计算机工程与应用2024，Vol.60Issue(8)：287-295,9.DOI:10.3778/j.issn.1002-8331.2304-0035
融合自注意力和图卷积的多视图群组推荐
Multi-View Group Recommendation Integrating Self-Attention and Graph Convolution
摘要
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 collabora-tive 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.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Key words
group recommendation/self-attention mechanism/graph convolution neural network/adaptive fusion分类
信息技术与安全科学引用本文复制引用
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):287-295,9.基金项目
国家自然科学基金面上项目(61772249). （61772249）
