1.8k citations
- Zhejiang UniversityCN48 papers
- Peking UniversityCN24 papers
- Tsinghua UniversityCN23 papers
- Alibaba Group (United States)US21 papers
- Shanghai Jiao Tong UniversityCN18 papers
- University of Science and Technology of ChinaCN17 papers
- Chinese Academy of SciencesCN14 papers
- Nanyang Technological UniversitySG10 papers
- University of Chinese Academy of SciencesCN10 papers
- Wuhan UniversityCN9 papers
- Hong Kong University of Science and TechnologyHK8 papers
- Huazhong University of Science and TechnologyCN8 papers
14 papers · 2 filters
DBRec: Dual-Bridging Recommendation via Discovering Latent Groups
Jingwei Ma, Jiahui Wen, Mingyang Zhong +6
In recommender systems, the user-item interaction data is usually sparse and not sufficient for learning comprehensive user/item representations for recommendation. To address this…
Conceptualize and Infer User Needs in E-commerce
Xusheng Luo, Yonghua Yang, Kenny Q. Zhu +2
Understanding latent user needs beneath shopping behaviors is critical to e-commercial applications. Without a proper definition of user needs in e-commerce, most industry solution…
Infer Implicit Contexts in Real-time Online-to-Offline Recommendation
Xichen Ding, Jie Tang, Tracy Liu +5
Understanding users' context is essential for successful recommendations, especially for Online-to-Offline (O2O) recommendation, such as Yelp, Groupon, and Koubei. Different from t…
A Capsule Network for Recommendation and Explaining What You Like and Dislike
Chenliang Li, Cong Quan, Li Peng +3
User reviews contain rich semantics towards the preference of users to features of items. Recently, many deep learning based solutions have been proposed by exploiting reviews for…
Query-based Interactive Recommendation by Meta-Path and Adapted Attention-GRU
Yu Zhu, Yu Gong, Qingwen Liu +6
Recently, interactive recommender systems are becoming increasingly popular. The insight is that, with the interaction between users and the system, (1) users can actively interven…
Sequential Scenario-Specific Meta Learner for Online Recommendation
Zhengxiao Du, Xiaowei Wang, Hongxia Yang +2
Cold-start problems are long-standing challenges for practical recommendations. Most existing recommendation algorithms rely on extensive observed data and are brittle to recommend…