摘要
跨域序列推荐(cross-domain sequential recommendation,CSR)旨在通过挖掘用户在多域混合序列中的行为偏好来为其提供跨域个性化推荐服务.近年来,研究人员开始尝试将图卷积网络(graph convolution network,GCN)集成到CSR中,以建模用户和项目之间的复杂关系.然而,基于图的CSR方法大多通过复杂的结构来捕捉用户在多个域中的序列行为模式,这导致其通常具有较高的计算复杂度和较大的内存开销,限制了模型在资源受限设备上的应用.此外,已有的轻量级图跨域序列推荐方法认为,应该采用单层聚合协议(single layer aggregating protocol,SLAP)来学习跨域序列图(cross-domain sequential graph,CSG)上的嵌入表示.基于这种协议的图卷积网络,能够规避多层聚合协议所带来的额外跨域噪声,但却难以捕捉域内的高阶序列依赖关系.为了解决上述挑战,提出了一种轻量级的三分支图外部注意力网络(tri-branches graph external attention network,TEA-Net).具体而言,TEA-Net首先将原始CSG分为域间以及域内序列图,并设计了一种并行的三分支图卷积网络结构来学习图中的节点表示.该结构能够以较低的计算开销,在不引入额外跨域噪声的条件下,学习域间的低阶协同过滤关系和域内的高阶序列依赖关系.其次,在三分支结构的基础上,提出了一种改良的外部注意力(external attention,EA)组件,该组件移除了EA中的非线性通道,使其能够以更低的开销挖掘项目序列依赖关系并将注意力权重在多个分支上共享.在 2个真实数据集上进行了广泛的实验来验证TEA-Net的性能表现.与 10种最先进的CSR方法相比,TEA-Net在轻量化性能和预测精度方面均取得了更好的结果.
Abstract
Cross-domain sequential recommendation(CSR)aims to capture the behavioral preferences of users by modeling their historical interaction sequences in multiple domains,thus providing personalized cross-domain recommendations.Recently,researchers have started integrating graph convolution networks(GCNs)into CSR to model complicated associations among users and items.However,due to their complicated structure,most graph-based CSR methods are usually accompanied by high computational complexity or memory overhead,making them difficult to deploy on resource-constrained edge devices.Besides,existing lightweight graph-based CSR methods tend to employ single layer aggregating protocol(SLAP)to propagate embeddings on cross-domain sequential graphs(CSG).Such a strategy indeed aids the GCNs in circumventing cross-domain noise interference caused by high-order neighborhood aggregation strategies.However,it also shields GCN from mining high-order sequential relationships within individual domains.To this end,we introduce a lightweight tri-branches graph external attention network(TEA-Net).Specifically,we separate the original CSG into three parts including two inner-domain sequential graphs and an inter-domain sequential graph and devise a parallel tri-branches graph convolution network to learn the node representations.This structure can simultaneously consider the first-order inter-domain correlations and the high-order inner-domain connectivity without introducing additional cross-domain noises.Besides,we propose an improved external attention(EA)component without the nonlinear channel,which captures the sequential dependency among items at a lower cost and shares attention weights across multiple branches.We conduct extensive experiments on two large-scale real-world datasets to verify the performance of TEA-Net.The experimental results demonstrate the superiority of TEA-Net in both the lightweight performance and the prediction accuracy compared with several state-of-the-art methods.
基金项目
国家自然科学基金项目(62072288)
山东省泰山学者计划项目(tsqn202211154)
山东省自然科学基金项目(ZR2022MF268)
NETL
NSTL
万方数据