Lerot: an online learning to rank framework

Online learning to rank methods for IR allow retrieval systems to optimize their own performance directly from interactions with users via click feedback. In the software package Lerot, presented in this paper, we have bundled all ingredients needed for experimenting with online learning to rank for IR. Lerot includes several online learning algorithms, interleaving methods and a full suite of ways to evaluate these methods. In the absence of real users, the evaluation method bundled in the software package is based on simulations of users interacting with the search engine. The software presented here has been used to verify findings of over six papers at major information retrieval venues over the last few years.

[1]  Katja Hofmann,et al.  Contextual Bandits for Information Retrieval , 2011 .

[2]  Katja Hofmann,et al.  Fast and reliable online learning to rank for information retrieval , 2013, SIGIR Forum.

[3]  Katja Hofmann,et al.  Reusing historical interaction data for faster online learning to rank for IR , 2013, DIR.

[4]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[5]  Nick Craswell,et al.  An experimental comparison of click position-bias models , 2008, WSDM '08.

[6]  Yi Chang,et al.  Yahoo! Learning to Rank Challenge Overview , 2010, Yahoo! Learning to Rank Challenge.

[7]  Katja Hofmann,et al.  Information Retrieval manuscript No. (will be inserted by the editor) Balancing Exploration and Exploitation in Listwise and Pairwise Online Learning to Rank for Information Retrieval , 2022 .

[8]  Thorsten Joachims,et al.  Optimizing search engines using clickthrough data , 2002, KDD.

[9]  Tie-Yan Liu,et al.  Learning to Rank for Information Retrieval , 2011 .

[10]  Filip Radlinski,et al.  Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search , 2007, TOIS.

[11]  Thorsten Joachims,et al.  Interactively optimizing information retrieval systems as a dueling bandits problem , 2009, ICML '09.

[12]  Katja Hofmann,et al.  Estimating interleaved comparison outcomes from historical click data , 2012, CIKM '12.

[13]  Katja Hofmann,et al.  A probabilistic method for inferring preferences from clicks , 2011, CIKM '11.

[14]  Katja Hofmann,et al.  Evaluating aggregated search using interleaving , 2013, CIKM.

[15]  Qiang Yang,et al.  Beyond ten blue links: enabling user click modeling in federated web search , 2012, WSDM '12.

[16]  Jaana Kekäläinen,et al.  Cumulated gain-based evaluation of IR techniques , 2002, TOIS.

[17]  Filip Radlinski,et al.  How does clickthrough data reflect retrieval quality? , 2008, CIKM '08.

[18]  Chao Liu,et al.  Efficient multiple-click models in web search , 2009, WSDM '09.

[19]  Gregory N. Hullender,et al.  Learning to rank using gradient descent , 2005, ICML.

[20]  Tong Zhang,et al.  Solving large scale linear prediction problems using stochastic gradient descent algorithms , 2004, ICML.

[21]  Tao Qin,et al.  LETOR: Benchmark Dataset for Research on Learning to Rank for Information Retrieval , 2007 .

[22]  Filip Radlinski,et al.  Optimized interleaving for online retrieval evaluation , 2013, WSDM.

[24]  D. Sculley,et al.  Large Scale Learning to Rank , 2009 .

[25]  Christos Faloutsos,et al.  Tailoring click models to user goals , 2009, WSCD '09.

[26]  Thorsten Joachims,et al.  Training linear SVMs in linear time , 2006, KDD '06.

[27]  ChengXiang Zhai,et al.  Evaluation of methods for relative comparison of retrieval systems based on clickthroughs , 2009, CIKM.