Y
Yu Gong
Researcher at Alibaba Group
Publications - 36
Citations - 1221
Yu Gong is an academic researcher from Alibaba Group. The author has contributed to research in topics: Graph (abstract data type) & Recommender system. The author has an hindex of 10, co-authored 34 publications receiving 850 citations. Previous affiliations of Yu Gong include Shanghai Jiao Tong University.
Papers
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Proceedings ArticleDOI
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
TL;DR: A unified framework takes advantage of both schools of thinking in information retrieval modelling and shows that the generative model learns to fit the relevance distribution over documents via the signals from the discriminative model to achieve a better estimation for document ranking.
Proceedings ArticleDOI
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
TL;DR: In this paper, a game theoretical minimax game is proposed to iteratively optimise both generative and discriminative models for document ranking, and the generative model is trained to fit the relevance distribution over documents via the signals from the discriminator.
Proceedings ArticleDOI
A Minimax Game for Instance based Selective Transfer Learning
Bo Wang,Minghui Qiu,Xisen Wang,Yaliang Li,Yu Gong,Xiaoyi Zeng,Jun Huang,Bo Zheng,Deng Cai,Jingren Zhou +9 more
TL;DR: This work proposes a general Minimax Game based model for selective transfer learning that outperforms the competing methods by a large margin and is shown to speed up the training process of the learning task in the target domain than traditional TL methods.
Proceedings ArticleDOI
Efficiently Solving the Practical Vehicle Routing Problem: A Novel Joint Learning Approach
TL;DR: This work proposes a strategy that combines the reinforcement learning manner with the supervised learning manner to train the model based on the graph convolutional network with node feature and edge feature as input and embedded.
Journal ArticleDOI
Deep Cascade Multi-task Learning for Slot Filling in Online Shopping Assistant
TL;DR: This paper proposed a multi-task model with cascade and residual connections, which jointly learns segment tagging, named entity tagging and slot filling in Chinese E-commerce shopping assistant dataset, achieving competitive accuracies on a standard dataset.