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- University of Science and Technology of ChinaCN8 papers
- Shanghai Jiao Tong UniversityCN7 papers
- Zhejiang UniversityCN7 papers
- CAS Key Laboratory of Urban Pollutant ConversionCN6 papers
- Peking UniversityCN5 papers
- Tsinghua UniversityCN5 papers
- Alibaba Group (United States)US4 papers
- University of Electronic Science and Technology of ChinaCN4 papers
- Association for Computing MachineryUS3 papers
- Heidelberg UniversityDE3 papers
- Nanyang Technological UniversitySG3 papers
- Chalmers University of TechnologySE2 papers
17 papers · 1 filter
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…