480 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Yong Chen11 · h 49
- Zhen-hua Zhao2 profiles11 · h 15
- Bing Chen2 profiles9 · h 10
- Huanshui Zhang2 profiles6 · h 45
- Q. Xie6 · h 10
- Shiqing Xin2 profiles6 · h 17
- Caifeng Shan5 · h 23
- Chuanzhong Li2 profiles5 · h 19
- W. Fan2 profiles5 · h 13
- Xiao-Zhao Chen5 · h 5
- Xiuzhen Cheng2 profiles5 · h 67
- Yan Xu2 profiles5 · h 88
- Shandong UniversityCN53 papers
- University of Science and Technology of ChinaCN29 papers
- Fudan UniversityCN27 papers
- Institute of Modern PhysicsCN26 papers
- Peking UniversityCN26 papers
- Chinese Academy of SciencesCN24 papers
- Beihang UniversityCN23 papers
- Nankai UniversityCN23 papers
- Shanghai Jiao Tong UniversityCN23 papers
- University of Maryland, College ParkUS22 papers
- Sun Yat-sen UniversityCN20 papers
- University of Shanghai for Science and TechnologyCN20 papers
10 papers · 1 filter
Lightweight yet Efficient: An External Attentive Graph Convolutional Network with Positional Prompts for Sequential Recommendation
Jinyu Zhang, Chao Li, Zhongying Zhao
Graph-based Sequential Recommender systems (GSRs) have gained significant research attention due to their ability to simultaneously handle user-item interactions and sequential rel…
Lightweight yet Fine-grained: A Graph Capsule Convolutional Network with Subspace Alignment for Shared-account Sequential Recommendation
Jinyu Zhang, Zhongying Zhao, Chao Li +1
Shared-account Sequential Recommendation (SSR) aims to provide personalized recommendations for accounts shared by multiple users with varying sequential preferences. Previous stud…
Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendation
Junchen Fu, Xuri Ge, Xin Xin +5
Multimodal foundation models (MFMs) have revolutionized sequential recommender systems through advanced representation learning. While Parameter-efficient Fine-tuning (PEFT) is com…
Towards Empathetic Conversational Recommender Systems
Xiaoyu Zhang, Ruobing Xie, Yougang Lyu +7
Conversational recommender systems (CRSs) are able to elicit user preferences through multi-turn dialogues. They typically incorporate external knowledge and pre-trained language m…
Unbiased and Robust: External Attention-enhanced Graph Contrastive Learning for Cross-domain Sequential Recommendation
Xinhua Wang, Houping Yue, Zizheng Wang +2
Cross-domain sequential recommenders (CSRs) are gaining considerable research attention as they can capture user sequential preference by leveraging side information from multiple…
HS-GCN: Hamming Spatial Graph Convolutional Networks for Recommendation
Han Liu, Yinwei Wei, Jianhua Yin +1
An efficient solution to the large-scale recommender system is to represent users and items as binary hash codes in the Hamming space. Towards this end, existing methods tend to co…
GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation
Jiajia Chen, Xin Xin, Xianfeng Liang +2
Generating recommendations based on user-item interactions and user-user social relations is a common use case in web-based systems. These connections can be naturally represented…
Dual Preference Distribution Learning for Item Recommendation
Xue Dong, Xuemeng Song, Na Zheng +2
Recommender systems can automatically recommend users with items that they probably like. The goal of them is to model the user-item interaction by effectively representing the use…
Adaptive Collaborative Similarity Learning for Unsupervised Multi-view Feature Selection
Xiao Dong, Lei Zhu, Xuemeng Song +2
In this paper, we investigate the research problem of unsupervised multi-view feature selection. Conventional solutions first simply combine multiple pre-constructed view-specific…
Mutual Clustering on Comparative Texts via Heterogeneous Information Networks
Jianping Cao, Senzhang Wang, Danyan Wen +3
Currently, many intelligence systems contain the texts from multi-sources, e.g., bulletin board system (BBS) posts, tweets and news. These texts can be ``comparative'' since they m…