26 citations · 54 across the 6 of their papers we have counts for
6 papers
CDSM: Cascaded Deep Semantic Matching on Textual Graphs Leveraging Ad-hoc Neighbor Selection
Jing Yao, Zheng Liu, Junhan Yang +3
Deep semantic matching aims to discriminate the relationship between documents based on deep neural networks. In recent years, it becomes increasingly popular to organize documents…
Hybrid Inverted Index Is a Robust Accelerator for Dense Retrieval
Peitian Zhang, Zheng Liu, Shitao Xiao +2
Inverted file structure is a common technique for accelerating dense retrieval. It clusters documents based on their embeddings; during searching, it probes nearby clusters w.r.t.…
Ultron: An Ultimate Retriever on Corpus with a Model-based Indexer
Yujia Zhou, Jing Yao, Zhicheng Dou +3
Document retrieval has been extensively studied within the index-retrieve framework for decades, which has withstood the test of time. Unfortunately, such a pipelined framework lim…
DynamicRetriever: A Pre-training Model-based IR System with Neither Sparse nor Dense Index
Yujia Zhou, Jing Yao, Zhicheng Dou +2
Web search provides a promising way for people to obtain information and has been extensively studied. With the surgence of deep learning and large-scale pre-training techniques, v…
Learning to Select Historical News Articles for Interaction based Neural News Recommendation
Peitian Zhang, Zhicheng Dou, Jing Yao
The key to personalized news recommendation is to match the user's interests with the candidate news precisely and efficiently. Most existing approaches embed user interests into a…
USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence
Jing Yao, Zhicheng Dou, Ruobing Xie +3
Search and recommendation are the two most common approaches used by people to obtain information. They share the same goal -- satisfying the user's information need at the right t…