A Multi-Head Attention Based Dual Target Graph Collaborative Filtering Network

IF 0.9 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Qinglong Peng, Bin Tang, Jinhuan Liu, Shuang Cui, Junwei Du, Yan Lu, Feng Jiang, Xu Yu
{"title":"A Multi-Head Attention Based Dual Target Graph Collaborative Filtering Network","authors":"Qinglong Peng, Bin Tang, Jinhuan Liu, Shuang Cui, Junwei Du, Yan Lu, Feng Jiang, Xu Yu","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00086","DOIUrl":null,"url":null,"abstract":"Recently, cross-domain collaborative filtering (CDCF) has been widely used to solve the data sparsity problem in recommendation systems. Therein, the dual-target cross-domain recommendation becomes a research hotspot, which aims to improve the recommendation performance of both target and source domains. Most existing approaches tend to use fixed weights or self-attention in a single representation space for the bi-directional inter-domain transfer of the user representation. However, a single representation space leads to limited representation capability, which makes the transfer of the user representation coarse-grained and inaccurate. In this paper, Multi-head Attention Based Dual Target Graph Collaborative Filtering Network (MA-DTGCF) is proposed. The core of the model is the bi-directional transfer graph convolution layer, consisting of a graph convolution layer and a bi-directional transfer layer based on a multi-head attention mechanism. The latter can achieve fine-grained and adaptive transfer of user features in multiple representation subspaces. It is worth noting that by stacking multiple bi-directional transfer graph convolutional layers, we can get high-order user and item features and achieve adaptive transfer of each order user features. Experimental results on three real datasets show that the proposed MA-DTGCF model significantly outperforms the state-of-the-art models in terms of HR and NDCG.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scalable Computing-Practice and Experience","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00086","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0

Abstract

Recently, cross-domain collaborative filtering (CDCF) has been widely used to solve the data sparsity problem in recommendation systems. Therein, the dual-target cross-domain recommendation becomes a research hotspot, which aims to improve the recommendation performance of both target and source domains. Most existing approaches tend to use fixed weights or self-attention in a single representation space for the bi-directional inter-domain transfer of the user representation. However, a single representation space leads to limited representation capability, which makes the transfer of the user representation coarse-grained and inaccurate. In this paper, Multi-head Attention Based Dual Target Graph Collaborative Filtering Network (MA-DTGCF) is proposed. The core of the model is the bi-directional transfer graph convolution layer, consisting of a graph convolution layer and a bi-directional transfer layer based on a multi-head attention mechanism. The latter can achieve fine-grained and adaptive transfer of user features in multiple representation subspaces. It is worth noting that by stacking multiple bi-directional transfer graph convolutional layers, we can get high-order user and item features and achieve adaptive transfer of each order user features. Experimental results on three real datasets show that the proposed MA-DTGCF model significantly outperforms the state-of-the-art models in terms of HR and NDCG.
基于多头注意的双目标图协同过滤网络
近年来,跨域协同过滤(CDCF)被广泛用于解决推荐系统中的数据稀疏性问题。其中,双目标跨域推荐成为一个研究热点,旨在提高目标域和源域的推荐性能。大多数现有方法倾向于在单个表示空间中使用固定权值或自关注来实现用户表示的双向域间传递。然而,单一的表示空间导致有限的表示能力,这使得用户表示的传输粗粒度和不准确。本文提出了一种基于多头注意力的双目标图协同过滤网络(MA-DTGCF)。该模型的核心是双向迁移图卷积层,由图卷积层和基于多头注意机制的双向迁移层组成。后者可以在多个表示子空间中实现用户特征的细粒度和自适应转移。值得注意的是,通过叠加多个双向传递图卷积层,我们可以得到高阶用户和物品特征,并实现每阶用户特征的自适应传递。在三个真实数据集上的实验结果表明,所提出的MA-DTGCF模型在HR和NDCG方面明显优于现有模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Scalable Computing-Practice and Experience
Scalable Computing-Practice and Experience COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.00
自引率
0.00%
发文量
10
期刊介绍: The area of scalable computing has matured and reached a point where new issues and trends require a professional forum. SCPE will provide this avenue by publishing original refereed papers that address the present as well as the future of parallel and distributed computing. The journal will focus on algorithm development, implementation and execution on real-world parallel architectures, and application of parallel and distributed computing to the solution of real-life problems.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信