Back to Search
Start Over
DeepRank
- Source :
- CIKM
- Publication Year :
- 2017
- Publisher :
- ACM, 2017.
-
Abstract
- This paper concerns a deep learning approach to relevance ranking in information retrieval (IR). Existing deep IR models such as DSSM and CDSSM directly apply neural networks to generate ranking scores, without explicit understandings of the relevance. According to the human judgement process, a relevance label is generated by the following three steps: 1) relevant locations are detected, 2) local relevances are determined, 3) local relevances are aggregated to output the relevance label. In this paper we propose a new deep learning architecture, namely DeepRank, to simulate the above human judgment process. Firstly, a detection strategy is designed to extract the relevant contexts. Then, a measure network is applied to determine the local relevances by utilizing a convolutional neural network (CNN) or two-dimensional gated recurrent units (2D-GRU). Finally, an aggregation network with sequential integration and term gating mechanism is used to produce a global relevance score. DeepRank well captures important IR characteristics, including exact/semantic matching signals, proximity heuristics, query term importance, and diverse relevance requirement. Experiments on both benchmark LETOR dataset and a large scale clickthrough data show that DeepRank can significantly outperform learning to ranking methods, and existing deep learning methods.<br />Comment: Published as a conference paper at CIKM 2017, CIKM'17, November 6--10, 2017, Singapore TextNet (https://github.com/pl8787/textnet-release) PyTorch (https://github.com/pl8787/DeepRank_PyTorch)
- Subjects :
- FOS: Computer and information sciences
Information retrieval
Artificial neural network
business.industry
Computer science
Deep learning
02 engineering and technology
Convolutional neural network
Computer Science - Information Retrieval
Ranking (information retrieval)
Ranking
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Benchmark (computing)
020201 artificial intelligence & image processing
Relevance (information retrieval)
Artificial intelligence
business
Heuristics
Information Retrieval (cs.IR)
Semantic matching
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
- Accession number :
- edsair.doi.dedup.....86d23c3580b0126cf9993678d1f3d28b