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Heterogeneous Hierarchical Feature Aggregation Network for Personalized Micro-Video Recommendation

文献类型：期刊论文

作者Cai, Desheng4; Qian, Shengsheng2,3; Fang, Quan2,3; Xu, Changsheng1,2,3
刊名IEEE TRANSACTIONS ON MULTIMEDIA
出版日期2022
卷号24页码:805-818
ISSN号1520-9210
关键词Graph neural networks Task analysis Semantics Aggregates Data structures Collaboration Visualization Heterogeneous graph micro-video recommendation multi-modal
DOI10.1109/TMM.2021.3059508
通讯作者Xu, Changsheng(csxu@nlpr.ia.ac.cn)
英文摘要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.
WOS关键词INFORMATION
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]
WOS研究方向Computer Science ; Telecommunications
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000753488100023
资助机构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
源URL[http://ir.ia.ac.cn/handle/173211/47891]  
专题自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
通讯作者Xu, Changsheng
作者单位1.Peng Cheng Lab, Shenzhen 518066, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
4.Hefei Univ Technol, Hefei 230009, Peoples R China
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.

入库方式： OAI收割

来源：自动化研究所

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