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Geometry Contrastive Learning on Heterogeneous Graphs



Shichao Zhu , Chuan Zhou , Anfeng Cheng , Shirui Pan , Shuaiqiang Wang , Dawei Yin , Bin Wang

* 24 pages, 3 figures 

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Are Graph Neural Networks Really Helpful for Knowledge Graph Completion?



Juanhui Li , Harry Shomer , Jiayuan Ding , Yiqi Wang , Yao Ma , Neil Shah , Jiliang Tang , Dawei Yin


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Practical Strategies of Active Learning to Rank for Web Search



Qingzhong Wang , Haifang Li , Haoyi Xiong , Wen Wang , Jiang Bian , Yu Lu , Shuaiqiang Wang , Zhicong Cheng , Jingbo Zhou , Dawei Yin , Dejing Dou

* 10 pages 

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ERNIE-Search: Bridging Cross-Encoder with Dual-Encoder via Self On-the-fly Distillation for Dense Passage Retrieval



Yuxiang Lu , Yiding Liu , Jiaxiang Liu , Yunsheng Shi , Zhengjie Huang , Shikun Feng Yu Sun , Hao Tian , Hua Wu , Shuaiqiang Wang , Dawei Yin , Haifeng Wang


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Hypergraph Contrastive Collaborative Filtering



Lianghao Xia , Chao Huang , Yong Xu , Jiashu Zhao , Dawei Yin , Jimmy Xiangji Huang

* This paper has been published as a full paper at SIGIR 2022 

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Incorporating Explicit Knowledge in Pre-trained Language Models for Passage Re-ranking



Qian Dong , Yiding Liu , Suqi Cheng , Shuaiqiang Wang , Zhicong Cheng , Shuzi Niu , Dawei Yin


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Graph Enhanced BERT for Query Understanding



Juanhui Li , Yao Ma , Wei Zeng , Suqi Cheng , Jiliang Tang , Shuaiqiang Wang , Dawei Yin


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Sequential Recommendation with User Evolving Preference Decomposition



Weiqi Shao , Xu Chen , Long Xia , Jiashu Zhao , Dawei Yin

* sequential recommendation 

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Contrastive Meta Learning with Behavior Multiplicity for Recommendation



Wei Wei , Chao Huang , Lianghao Xia , Yong Xu , Jiashu Zhao , Dawei Yin

* Published as a full paper at WSDM 2022 and Awarded as Best Paper Candidate 

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Sequential Recommendation with Adaptive Preference Disentanglement



Weiqi Shao , Xu Chen , Jiashu Zhao , Long Xia , Dawei Yin

* In this paper, authors proposed a sequential recommender which can disentangle different user preferences from given user historical sequences. Authors propose a behavior allocator block to split the single sequence into multiple sub-sequences, which can be optimized with reinforcement learning methods 

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