基于胶囊图卷积的解缠绕会话感知推荐方法  

Capsule-based graph convolutional disentanglement for session-aware recommendation

作　　者：陶玉合 高榕 邵雄凯[1] 吴歆韵 李晶[3] Tao Yuhe;Gao Rong;Shao Xiongkai;Wu Xinyun;Li Jing(School of Computer Science,Hubei University of Technology,Wuhan 430068,China;State Key Laboratory of New Computer Software Technology,Nanjing University,Nanjing 210093,China;School of Computer Science,Wuhan University,Wuhan 430068,China)

机构地区：[1]湖北工业大学计算机学院,武汉430068 [2]南京大学计算机软件新技术国家重点实验室,南京210093 [3]武汉大学计算机学院,武汉430068

出　　处：《计算机应用研究》2023年第1期122-128,共7页Application Research of Computers

基　　金：国家自然科学基金资助项目(61902116);南京大学计算机软件新技术国家重点实验室开放课题(KFKT2021B12);湖北省高层次人才基金资助项目(GCRC2020011);湖北工业大学博士科研启动基金资助项目(BSQD2019026,BSQD2019022)。

摘　　要：针对会话推荐模型中存在的推荐准确率不高的问题,提出了一种基于胶囊图卷积的解缠绕会话感知推荐方法(CGCD)。具体来说,采用解缠绕学习技术将项目嵌入转换为基于多个子通道的因子嵌入,利用图卷积网络对因子嵌入进行细粒度的学习。然后,利用胶囊动态融合策略聚合不同的因子获得新的项目嵌入。此外,采用多头注意力机制为会话中每个项目分配权重。最后,根据分配的权重将项目嵌入与当前会话中的其他项目进行聚合,进而生成准确的会话表示,实现项目推荐。在两个公开真实数据集上的实验表明,所提模型在推荐的Pre@10,Pre@20,MRR@10和MRR@20上平均提高了5.17%、2.99%、6.56%和2.94%,验证了其有效性与高效性。To address the problem of poor recommendation accuracy in the session recommendation model,this paper proposed a capsule-based graph convolutional disentanglement for session-aware recommendation(CGCD)method.Specifically,it used disentangled learning technique to transition item embedding into factor embedding based on multiple sub-channels.Then,it used a capsule dynamic fusion strategy to aggregate different factors to obtain a new item embedding.In addition,it used a multi-head attention mechanism to assign weights to each item in the session.Finally,it aggregated the item embedding with other items in the current session according to the assigned weights,and then generated an accurate session representation to achieve item recommendation.Experiments on two publicly available real datasets show that the proposed model can improve the Pre@10,Pre@20,MRR@10 and MRR@20 by 5.17%,2.99%,6.56%and 2.94%on average of the recommended performance,which verifies the effectiveness and efficiency of the method in this paper.

关 键 词：推荐系统 深度学习 图卷积网络 注意力机制 胶囊网络 用户意图 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]