基于多嵌入融合的top-N推荐  

Multi-embedding Fusion Based on top- N Recommendation

作　　者：杨真真[1] 王东涛 杨永鹏[1,2] 华仁玉 YANG Zhenzhen;WANG Dongtao;YANG Yongpeng;HUA Renyu(Key Laboratory of Ministry of Education in Broadband Wireless Communication and Sensor Network Technology,Nanjing University of Posts and Telecommunications,Nanjing 210023,China;School of Network and Communication,Nanjing Vocational College of Information Technology,Nanjing 210023,China)

机构地区：[1]南京邮电大学宽带无线通信与传感网技术教育部重点实验室,南京210023 [2]南京信息职业技术学院网络与通信学院,南京210023

出　　处：《计算机科学》2024年第7期140-145,共6页Computer Science

基　　金：国家自然科学基金(62171232);南京邮电大学宽带无线通信与传感网技术教育部重点实验室开放研究基金(JZNY202113);南京邮电大学科研项目(NY220207);江苏省研究生科研与实践创新计划项目(KYCX22_0955,SJCX23_0251)。

摘　　要：异构信息网络(Heterogeneous Information Network, HIN)凭借其丰富的语义信息和结构信息被广泛应用于推荐系统中,虽然取得了很好的推荐效果,但较少考虑局部特征放大、信息交互和多嵌入聚合等问题。针对这些问题,提出了一种新的用于top-N推荐的多嵌入融合推荐(Multi-embedding Fusion Recommendation, MFRec)模型。首先,该模型在用户和项目学习分支中都采用对象上下文表示网络,充分利用上下文信息以放大局部特征,增强相邻节点的交互性;其次,将空洞卷积和空间金字塔池化引入元路径学习分支,以便获取多尺度信息并增强元路径的节点表示;然后,采用多嵌入融合模块以便更好地进行用户、项目以及元路径的嵌入融合,细粒度地进行多嵌入之间的交互学习,并强调了各特征的不同重要性程度;最后,在两个公共推荐系统数据集上进行了实验,结果表明所提模型MFRec优于现有的其他top-N推荐系统模型。Heterogeneous information network(HIN)is widely used in recommender systems since its rich semantic and structu-ral information.Although the HIN and the network embedding have achieved good results in recommender systems,the local feature amplification,the interaction of embedding vectors,and the multi-embedding aggregation methods have not been fully consi-dered.To overcome these problems,a new multi-embedding fusion recommendation(MFRec)model is proposed.Firstly,object-contextual representation network is introduced to both branches of user and node representation learning to amplify local features and enhance the interaction of neighbor nodes.Subsequently,the dilated convolution and the spatial pyramid pooling are introduced to the meta-paths learning to obtain multi-scale information and enhance the representation of meta-paths.In addition,the multi-embedding fusion module is introduced to better carry out the embedding fusion of users,items and meta-paths.The interaction between embeddings is carried out in a fine-grained way,and the different importance of each feature is emphasized.Finally,experimental results on two public recommendation system datasets show that the proposed MFRec has better performance than other existing top-N recommendation models.

关 键 词：异构信息网络 推荐系统 top-N推荐 多嵌入融合 注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]