Results 61 to 70 of about 276,223 (97)

Invariant Representation Learning for Multimedia Recommendation

ACM Multimedia, 2022
Multimedia recommendation forms a personalized ranking task with multimedia content representations which are mostly extracted via generic encoders. However, the generic representations introduce spurious correlations --- the meaningless correlation from
Xiaoyu Du, Zike Wu, Fuli Feng, Xiangnan He, Jinhui Tang   +4 more
semanticscholar   +1 more source

Learning Hybrid Behavior Patterns for Multimedia Recommendation

ACM Multimedia, 2022
Multimedia recommendation aims to predict user preferences where users interact with multimodal items. Collaborative filtering based on graph convolutional networks manifests impressive performance gains in multimedia recommendation.
Zongshen Mu, Yueting Zhuang, Jie Tan, Jun Xiao, Siliang Tang   +4 more
semanticscholar   +1 more source

Breaking Isolation: Multimodal Graph Fusion for Multimedia Recommendation by Edge-wise Modulation

ACM Multimedia, 2022
In a multimedia recommender system, rich multimodal dynamics of user-item interactions are worth availing ourselves of and have been facilitated by Graph Convolutional Networks (GCNs).
Feiyu Chen, Junjie Wang, Yin-wei Wei, Haitao Zheng, Jie Shao   +4 more
semanticscholar   +1 more source

Hierarchical User Intent Graph Network for Multimedia Recommendation

IEEE transactions on multimedia, 2021
Understanding user preference on item context is the key to acquire a high-quality multimedia recommendation. Typically, the pre-existing features of items are derived from pre-trained models (e.g.
Yin-wei Wei, Xiang Wang, Xiangnan He, Liqiang Nie, Yong Rui, Tat-Seng Chua   +5 more
semanticscholar   +1 more source

Data-Free Ensemble Knowledge Distillation for Privacy-conscious Multimedia Model Compression

ACM Multimedia, 2021
Recent advances in deep learning bring impressive performance for multimedia applications. Hence, compressing and deploying these applications on resource-limited edge devices via model compression becomes attractive.
Zhiwei Hao, Yong Luo, Han Hu, Jianping An, Yonggang Wen   +4 more
semanticscholar   +1 more source

Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback

ACM Multimedia, 2020
Reorganizing implicit feedback of users as a user-item interaction graph facilitates the applications of graph convolutional networks (GCNs) in recommendation tasks. In the interaction graph, edges between user and item nodes function as the main element
Yin-wei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Tat-Seng Chua   +4 more
semanticscholar   +1 more source

Efficient Supervised Discrete Multi-View Hashing for Large-Scale Multimedia Search

IEEE transactions on multimedia, 2020
Hashing has recently received substantial attention in large-scale multimedia search for its extremely low-cost storage cost and high retrieval efficiency.
Xu Lu, Lei Zhu, Jingjing Li, Huaxiang Zhang, Heng Tao Shen   +4 more
semanticscholar   +1 more source

Hybrid Deep-Learning-Based Anomaly Detection Scheme for Suspicious Flow Detection in SDN: A Social Multimedia Perspective

IEEE transactions on multimedia, 2019
The continuous development and usage of multi-media-based applications and services have contributed to the exponential growth of social multimedia traffic.
S. Garg, K. Kaur, Neeraj Kumar, J. Rodrigues   +3 more
semanticscholar   +1 more source

How to Learn Item Representation for Cold-Start Multimedia Recommendation?

ACM Multimedia, 2020
The ability of recommending cold items (that have no behavior history) is a core strength of multimedia recommendation compared with behavior-only collaborative filtering.
Xiaoyu Du, Xiang Wang, Xiangnan He, Zechao Li, Jinhui Tang, Tat-Seng Chua   +5 more
semanticscholar   +1 more source

Multimedia Intelligence: When Multimedia Meets Artificial Intelligence

IEEE transactions on multimedia, 2020
Owing to the rich emerging multimedia applications and services in the past decade, super large amount of multimedia data has been produced for the purpose of advanced research in multimedia.
Wenwu Zhu, Xin Wang, Wen Gao
semanticscholar   +1 more source