143 citations · 506 across the 22 of their papers we have counts for
6 papers · 1 filter
Noise-Robust Dense Retrieval via Contrastive Alignment Post Training
Daniel Campos, ChengXiang Zhai, Alessandro Magnani
The success of contextual word representations and advances in neural information retrieval have made dense vector-based retrieval a standard approach for passage and document rank…
Dense Sparse Retrieval: Using Sparse Language Models for Inference Efficient Dense Retrieval
Daniel Campos, ChengXiang Zhai
Vector-based retrieval systems have become a common staple for academic and industrial search applications because they provide a simple and scalable way of extending the search to…
On Application of Learning to Rank for E-Commerce Search
Shubhra Kanti Karmaker Santu, Parikshit Sondhi, ChengXiang Zhai
E-Commerce (E-Com) search is an emerging important new application of information retrieval. Learning to Rank (LETOR) is a general effective strategy for optimizing search engines,…
Modeling Diverse Relevance Patterns in Ad-hoc Retrieval
Yixing Fan, Jiafeng Guo, Yanyan Lan +3
Assessing relevance between a query and a document is challenging in ad-hoc retrieval due to its diverse patterns, i.e., a document could be relevant to a query as a whole or parti…
Numerical Facet Range Partition: Evaluation Metric and Methods
Xueqing Liu, Chengxiang Zhai, Wei Han +1
Faceted navigation is a very useful component in today's search engines. It is especially useful when user has an exploratory information need or prefer certain attribute values th…
Preference-based Graphic Models for Collaborative Filtering
Rong Jin, Luo Si, ChengXiang Zhai
Collaborative filtering is a very useful general technique for exploiting the preference patterns of a group of users to predict the utility of items to a particular user. Previous…