R
Ruiming Tang
Researcher at Huawei
Publications - 178
Citations - 4429
Ruiming Tang is an academic researcher from Huawei. The author has contributed to research in topics: Computer science & Recommender system. The author has an hindex of 19, co-authored 116 publications receiving 2294 citations. Previous affiliations of Ruiming Tang include The Chinese University of Hong Kong & National University of Singapore.
Papers
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Proceedings ArticleDOI
U-rank: Utility-oriented Learning to Rank with Implicit Feedback
Xinyi Dai,Jiawei Hou,Qing Liu,Yunjia Xi,Ruiming Tang,Weinan Zhang,Xiuqiang He,Jun Wang,Yong Yu +8 more
TL;DR: U-Rank as mentioned in this paper proposes a ranking framework that directly optimizes the expected utility of the ranking list with a position-aware deep click-through rate prediction model, which addresses the attention bias considering both query-level and item-level features.
Proceedings ArticleDOI
Multi-Branch Convolutional Network for Context-Aware Recommendation
TL;DR: A Multi-Branch Convolutional Network (MBCN) which includes three branches: the standard convolutional layer, the dilated convolutionAL layer and the bias layer is proposed which is able to explicitly model feature interactions with arbitrary orders at the vector-wise, which fully express context-aware feature semantics.
Proceedings ArticleDOI
Unsupervised Learning Style Classification for Learning Path Generation in Online Education Platforms
TL;DR: A novel Deep Unsupervised Classifier with domain Knowledge (DUCK) is proposed to convert the discovered conclusions and domain knowledge into learnable model components (which addresses both C1 and C2), which significantly improves the effectiveness, efficiency, and robustness.
Journal ArticleDOI
Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation
TL;DR: A novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning (CKML) framework to learn shared and behavior-specific interests for different behaviors, which addresses limitations of state-of-the-art multi-behavior models.
Proceedings ArticleDOI
An Efficient and Truthful Pricing Mechanism for Team Formation in Crowdsourcing Markets
TL;DR: In this article, four incentive mechanisms for selecting workers to form a valid team (that can complete the task) and determining each individual worker's payment are presented. And they examine profitability, individual rationality, computational efficiency, and truthfulness for each of the four mechanisms.