Fetching the paper…
Reading the bibliography…
E-Commerce (E-Com) search is an emerging important new application of information retrieval.
Individual comparisons by ranking methods
Frank Wilcoxon. 1945 · 1945
Earlier work this paper cites.
Okapi at TREC-3
Stephen E Robertson, Steve Walker, Susan Jones, Micheline M Hancock-Beaulieu, Mike Gatford, and others. 1995 · 1995
Earlier work this paper cites.
Support vector regression machines
Alex Smola and Vladimir Vapnik. 1997 · 1997
Earlier work this paper cites.
Least squares support vector machine classifiers
Johan AK Suykens and Joos Vandewalle. 1999 · 1999
Earlier work this paper cites.
Random forests
Leo Breiman. 2001 · 2001
Earlier work this paper cites.
Cumulated gain-based evaluation of IR techniques
Kalervo Järvelin and Jaana Kekäläinen. 2002 · 2002
Earlier work this paper cites.
An efficient boosting algorithm for combining preferences
Yoav Freund, Raj Iyer, Robert E Schapire, and Yoram Singer. 2003 · 2003
Earlier work this paper cites.
Learning to rank using gradient descent. In Proceedings of the 22nd international conference on Machine learning
Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender. 2005 · 2005
Earlier work this paper cites.
Efficient l1 regularized logistic regression. In Proceedings of the National Conference on Artificial Intelligence
Su-In Lee, Honglak Lee, Pieter Abbeel, and Andrew Y Ng. 2006 · 2006
Earlier work this paper cites.
Adarank: a boosting algorithm for information retrieval. In ACM SIGIR
Jun Xu and Hang Li. 2007 · 2007
Earlier work this paper cites.
Crowdsourcing for relevance evaluation. In ACM SigIR Forum
Omar Alonso, Daniel E Rose, and Benjamin Stewart. 2008 · 2008
Earlier work this paper cites.
LIBLINEAR: A library for large linear classification
Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, and Chih-Jen Lin. 2008 · 2008
Earlier work this paper cites.
Trust region newton method for logistic regression
Chih-Jen Lin, Ruby C Weng, and S Sathiya Keerthi. 2008 · 2008
Cited alongside, same era.
Relevance criteria for e-commerce: a crowdsourcing-based experimental analysis. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Omar Alonso and Stefano Mizzaro. 2009 · 2009
Cited alongside, same era.
Learning to rank for information retrieval
Tie-Yan Liu. 2009 · 2009
Cited alongside, same era.
From ranknet to lambdarank to lambdamart: An overview
Christopher JC Burges. 2010 · 2010
Cited alongside, same era.
Ensuring quality in crowdsourced search relevance evaluation: The effects of training question distribution. In SIGIR 2010 workshop on crowdsourcing for search evaluation
John Le, Andy Edmonds, Vaughn Hester, and Lukas Biewald. 2010 · 2010
Cited alongside, same era.
Enhancing Product Search by Best-selling Prediction in e-Commerce. In CIKM ’12
Bo Long, Jiang Bian, Anlei Dong, and Yi Chang. 2012 · 2012
Later among the works it cites.
On the usefulness of query features for learning to rank. In Proceedings of the 21st ACM international conference on Information and knowledge management
Craig Macdonald, Rodrygo LT Santos, and Iadh Ounis. 2012 · 2012
Later among the works it cites.
Supporting Keyword Search in Product Database: A Probabilistic Approach
Huizhong Duan, ChengXiang Zhai, Jinxing Cheng, and Abhishek Gattani. 2013 · 2013
Later among the works it cites.
The whens and hows of learning to rank for web search
Craig Macdonald, Rodrygo LT Santos, and Iadh Ounis. 2013 · 2013
Later among the works it cites.
Introducing LETOR 4.0 Datasets
Tao Qin and Tie-Yan Liu. 2013 · 2013
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Using BM25F for semantic search. In Proceedings of the 3rd international semantic search workshop
José R Pérez-Agüera, Javier Arroyo, Jane Greenberg, Joaquin Perez Iglesias, and Victor Fresno. 2010 · 2010
Cited alongside, same era.
On the Choice of Effectiveness Measures for Learning to Rank
Emine Yilmaz and Stephen Robertson. 2010 · 2010
Cited alongside, same era.
Yahoo! Learning to Rank Challenge Overview.. In Yahoo! Learning to Rank Challenge
Olivier Chapelle and Yi Chang. 2011 · 2011
Cited alongside, same era.
Future directions in learning to rank.. In Yahoo! Learning to Rank Challenge
Olivier Chapelle, Yi Chang, and Tie-Yan Liu. 2011 · 2011
Cited alongside, same era.
Diversifying Product Search Results. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval
Xiangru Chen, Haofen Wang, Xinruo Sun, Junfeng Pan, and Yong Yu. 2011 · 2011
Cited alongside, same era.
Towards a Theory Model for Product Search. In Proceedings of the 20th International Conference on World Wide Web
Beibei Li, Anindya Ghose, and Panagiotis G. Ipeirotis. 2011 · 2011
Cited alongside, same era.
Smoothing techniques: with implementation in S
Wolfgang Härdle. 2012 · 2012
Cited alongside, same era.
Facet Selection Algorithms for Web Product Search. In Proceedings of the 22Nd ACM International Conference on Information & Knowledge Management
Damir Vandic, Flavius Frasincar, and Uzay Kaymak. 2013 · 2013
Later among the works it cites.
Learning to rank for information retrieval and natural language processing
Hang Li. 2014 · 2014
Later among the works it cites.
Latent Dirichlet Allocation Based Diversified Retrieval for e-Commerce Search. In WSDM ’14
Jun Yu, Sunil Mohan, Duangmanee (Pew) Putthividhya, and Weng-Keen Wong. 2014 · 2014
Later among the works it cites.
A cross-benchmark comparison of 87 learning to rank methods
Niek Tax, Sander Bockting, and Djoerd Hiemstra. 2015 · 2015
Later among the works it cites.
Advances in Formal Models of Search and Search Behaviour. In Proceedings of the 2016 ACM on International Conference on the Theory of Information Retrieval
Leif Azzopardi and Guido Zuccon. 2016 · 2016
Later among the works it cites.
An Empirical Study on Recommendation with Multiple Types of Feedback. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Liang Tang, Bo Long, Bee-Chung Chen, and Deepak Agarwal. 2016 · 2016
Later among the works it cites.
Learning Latent Vector Spaces for Product Search. In Proceedings of the 25th ACM CIKM ’16
Christophe Van Gysel, Maarten de Rijke, and Evangelos Kanoulas. 2016 · 2016
Later among the works it cites.