An experimental comparison of click position-bias models
- Nick Craswell, O. Zoeter, Michael J. Taylor, Bill Ramsey
- Computer ScienceWeb Search and Data Mining
- 11 February 2008
A cascade model, where users view results from top to bottom and leave as soon as they see a worthwhile document, is the best explanation for position bias in early ranks.
Random walks on the click graph
- Nick Craswell, M. Szummer
- Computer ScienceAnnual International ACM SIGIR Conference on…
- 23 July 2007
A Markov random walk model is applied to a large click log, producing a probabilistic ranking of documents for a given query, demonstrating its ability to retrieve relevant documents that have not yet been clicked for that query and rank those effectively.
Learning to Match using Local and Distributed Representations of Text for Web Search
- Bhaskar Mitra, Fernando Diaz, Nick Craswell
- Computer ScienceThe Web Conference
- 26 October 2016
This work proposes a novel document ranking model composed of two separate deep neural networks, one that matches the query and the document using a local representation, and another that Matching with distributed representations complements matching with traditional local representations.
Overview of the TREC 2020 Deep Learning Track
- Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Fernando Campos, E. Voorhees
- Computer ScienceText Retrieval Conference
- 17 March 2020
The Deep Learning Track is a new track for TREC 2019, with the goal of studying ad hoc ranking in a large data regime. It is the first track with large human-labeled training sets, introducing two…
Overview of the TREC 2009 Web Track
- C. Clarke, Nick Craswell, I. Soboroff
- Computer ScienceText Retrieval Conference
- 1 November 2009
An overview of the TREC 2009 Web Track is provided, including topic development, evaluation measures, and results, including both a traditional ad hoc retrieval task and a new diversity task.
Mean Reciprocal Rank
- Nick Craswell
- Computer Science, MathematicsEncyclopedia of Database Systems
- 2009
Overview of the TREC 2005 Enterprise Track
- Nick Craswell, A. D. Vries, I. Soboroff
- Computer ScienceText Retrieval Conference
- 2005
The goal of the enterprise track is to conduct experiments with enterprise data that reflect the experiences of users in real organisations, such that for example, an email ranking technique that is effective here would be a good choice for deployment in a real multi-user email search application.
A Theoretical Framework for Conversational Search
- Filip Radlinski, Nick Craswell
- Computer ScienceConference on Human Information Interaction and…
- 7 March 2017
This paper studies conversational approaches to information retrieval, presenting a theory and model of information interaction in a chat setting, and shows that while theoretical, the model could be practically implemented to satisfy the desirable properties presented.
Overview of the TREC-2001 Web track
- D. Hawking, Nick Craswell
- Computer Science
- 2002
TREC-2001 saw the falling into abeyance of the Large Web Task but a strengthening and broadening of activities based on the 1.69 million page WTlOg corpus. There were two tasks. The topic relevance…
TREC Complex Answer Retrieval Overview
- Laura Dietz, Manisha Verma, Filip Radlinski, Nick Craswell
- Computer ScienceText Retrieval Conference
- 2018
It is seen that combining traditional methods with learning-to-rank can outperform neural methods, even when many training queries are available, in TREC Complex Answer Retrieval.
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