11 citations · 32 across the 6 of their papers we have counts for
7 papers · 1 filter
SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval
Liang Pang, Jun Xu, Qingyao Ai +3
In learning-to-rank for information retrieval, a ranking model is automatically learned from the data and then utilized to rank the sets of retrieved documents. Therefore, an ideal…
Beyond Precision: A Study on Recall of Initial Retrieval with Neural Representations
Yan Xiao, Jiafeng Guo, Yixing Fan +3
Vocabulary mismatch is a central problem in information retrieval (IR), i.e., the relevant documents may not contain the same (symbolic) terms of the query. Recently, neural repres…
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…
Learning Visual Features from Snapshots for Web Search
Yixing Fan, Jiafeng Guo, Yanyan Lan +3
When applying learning to rank algorithms to Web search, a large number of features are usually designed to capture the relevance signals. Most of these features are computed based…
A Deep Investigation of Deep IR Models
Liang Pang, Yanyan Lan, Jiafeng Guo +2
The effective of information retrieval (IR) systems have become more important than ever. Deep IR models have gained increasing attention for its ability to automatically learning…
A Study of MatchPyramid Models on Ad-hoc Retrieval
Liang Pang, Yanyan Lan, Jiafeng Guo +2
Deep neural networks have been successfully applied to many text matching tasks, such as paraphrase identification, question answering, and machine translation. Although ad-hoc ret…