Heterogeneous Hierarchical Feature Aggregation Network for Personalized Micro-Video Recommendation

doi:10.1109/TMM.2021.3059508

	Heterogeneous Hierarchical Feature Aggregation Network for Personalized Micro-Video Recommendation
	Cai, Desheng 1; Qian, Shengsheng2,3 ; Fang, Quan2,3 ; Xu, Changsheng2,3,4
发表期刊	IEEE TRANSACTIONS ON MULTIMEDIA
ISSN	1520-9210
	2022
卷号	24 页码:805-818
通讯作者	Xu, Changsheng(csxu@nlpr.ia.ac.cn)
摘要	Micro-video recommendation has attracted extensive research attention with the increasing popularity of micro-video sharing platforms. Traditional approaches consider micro-video recommendation as a matching task and ignore the rich relationships among users and micro-videos from various modalities (e.g., visual, acoustic, and textual). Recently, GNN-based approaches show promising performance for the micro-video recommendation task. However, they mainly focus on the homogeneous graph which includes only one type of nodes or relations, and cannot be applied to the heterogeneous graph which consists of users, micro-videos, and related multi-modal information. In this paper, a novel Heterogeneous Hierarchical Feature Aggregation Network (HHFAN) is proposed for personalized micro-video recommendation. Our goal is to explore the highly complicated relationship information among users, micro-videos and related multi-modal information from a modality-aware Heterogeneous Information Graph (M-HIG), and thus generate high-quality user and micro-video embeddings for recommendation. The proposed model consists of two key components: (1) In data structure level, we build a heterogeneous graph and utilize a random walk based sampling strategy to sample neighbors for users and micro-videos. (2) In representation learning level, we design a hierarchical feature aggregation network including the intra- and inter-type feature aggregation networks to better capture the complex structure and rich semantic information in the heterogeneous graph. We evaluate our method on two real-world datasets and the results demonstrate that the proposed model outperforms the baseline methods.
关键词	Graph neural networks Task analysis Semantics Aggregates Data structures Collaboration Visualization Heterogeneous graph micro-video recommendation multi-modal
DOI	10.1109/TMM.2021.3059508
关键词[WOS]	INFORMATION
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFB1002804] ; National Natural Science Foundation of China[62036012] ; National Natural Science Foundation of China[6207072426] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[61572503] ; National Natural Science Foundation of China[61802405] ; National Natural Science Foundation of China[61872424] ; National Natural Science Foundation of China[61702509] ; National Natural Science Foundation of China[61832002] ; National Natural Science Foundation of China[61936005] ; National Natural Science Foundation of China[U1705262] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSWJSC039] ; K.C. Wong Education Foundation ; CCF-Tencent Open Fund ; Open Research Projects of Zhejiang Laboratory[2021KE0AB05]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Key Research Program of Frontier Sciences, CAS ; K.C. Wong Education Foundation ; CCF-Tencent Open Fund ; Open Research Projects of Zhejiang Laboratory
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS记录号	WOS:000753488100023
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	推荐系统
引用统计	被引频次：20[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/47892
专题	多模态人工智能系统全国重点实验室_多媒体计算
通讯作者	Xu, Changsheng
作者单位	1.Hefei Univ Technol, Hefei 230009, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Peng Cheng Lab, Shenzhen 518066, Peoples R China
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Cai, Desheng,Qian, Shengsheng,Fang, Quan,et al. Heterogeneous Hierarchical Feature Aggregation Network for Personalized Micro-Video Recommendation[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2022,24:805-818.
APA	Cai, Desheng,Qian, Shengsheng,Fang, Quan,&Xu, Changsheng.(2022).Heterogeneous Hierarchical Feature Aggregation Network for Personalized Micro-Video Recommendation.IEEE TRANSACTIONS ON MULTIMEDIA,24,805-818.
MLA	Cai, Desheng,et al."Heterogeneous Hierarchical Feature Aggregation Network for Personalized Micro-Video Recommendation".IEEE TRANSACTIONS ON MULTIMEDIA 24(2022):805-818.