Kilian Q. Weinberger

Learning to Rank

The goal of learning to rank is to develop machine learning algorithms that learn preferred orderings from experience. This is a particular important problem in web-search. Given a query consisting of a few words, the task is to accurately predict which web-page the user wants to obtain from the web. My research has focussed primarily on extensions of boosted regression trees, which have shown to be arguably the best algorithms for web-ranking. (All winning teams of the Yahoo Learning to Rank Challenge used boosted regression trees in one form or another.)

Check out our implementation of boosted regression tree and random forests.

Relevant publications:

[PDF][CODE][BIBTEX] Stephen Tyree, Kilian Q. Weinberger, Kunal Agrawal, Jennifer Paykin. Parallel Boosted Regression Trees for Web Search Ranking. Proceedings of the 20th international conference on World Wide Web (WWW-11), pages 387-396, ACM, New York, USA, 2011.

[PDF][CODE][BIBTEX] Ananth Mohan, Zheng Chen, Kilian Q. Weinberger. Web-Search Ranking with Initialized Gradient Boosted Regression Trees. Journal of Machine Learning Research, Workshop and Conference Proceedings 14, Yahoo! Learning to Rank Challenge, pages 77-89, MIT Press, 2011.

[PDF][BIBTEX] B. Bai, J. Weston, D. Grangier, R. Collobert, K. Sadamasa, Y. Qi, O. Chapelle, K. Q. Weinberger. Learning to Rank with (a Lot of) Word Features. Journal of Information Retrieval. Special Issue on Learning to Rank for Information Retrieval 13(3): 291-314. Springer Verlag, 2010.

[PDF][BIBTEX] Olivier Chapelle, Pannagadatta Shivaswamy, Srinivas Vadrevu, Kilian Q. Weinberger, Ya Zhang, Belle Tseng. Multi-Task Learning for Boosting with Application to Web Search Ranking. Machine Learning Journal, ISSN 0885-6125, pages 1-25, Springer Verlag, 2011.

[PDF][BIBTEX] B. Bai, J. Weston, D. Grangier, R. Collobert, O. Chapelle, K. Q. Weinberger. Supervised Semantic Indexing. The 18th ACM Conference on Information and Knowledge Management (CIKM), 2009.

[PDF][BIBTEX] E. Hörster, M. Slaney, M. Ranzato, K. Q. Weinberger (2009). Unsupervised Image Ranking. ACM Workshop on Web-Scale Multimedia Corpus (WSMC 2009), Beijing, China, October 2009