Online Learning to Rank for Cross-Language Information Retrieval

Online learning to rank for information retrieval has shown great promise in optimization of Web search results based on user interactions. However, online learning to rank has been used only in the monolingual setting where queries and documents are in the same language. In this work, we present the first empirical study of optimizing a model for Cross-Language Information Retrieval (CLIR) based on implicit feedback inferred from user interactions. We show that ranking models for CLIR with acceptable performance can be learned in an online setting although ranking features are noisy because of the language mismatch.

[1]  Gregory Grefenstette,et al.  Cross-Language Information Retrieval , 1998, The Springer International Series on Information Retrieval.

[2]  Mark Sanderson,et al.  Test Collection Based Evaluation of Information Retrieval Systems , 2010, Found. Trends Inf. Retr..

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

[4]  Wei Gao,et al.  Exploiting Bilingual Information to Improve Web Search , 2009, ACL.

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

[6]  Tao Qin,et al.  LETOR: A benchmark collection for research on learning to rank for information retrieval , 2010, Information Retrieval.

[7]  Azadeh Shakery,et al.  Using Learning to Rank Approach for Parallel Corpora Based Cross Language Information Retrieval , 2012, ECAI.

[8]  Jörg Tiedemann,et al.  Parallel Data, Tools and Interfaces in OPUS , 2012, LREC.

[9]  Ming-Feng Tsai,et al.  A study of learning a merge model for multilingual information retrieval , 2008, SIGIR '08.

[10]  Tie-Yan Liu,et al.  Learning to rank: from pairwise approach to listwise approach , 2007, ICML '07.

[11]  Tie-Yan Liu,et al.  Learning to rank for information retrieval , 2009, SIGIR.

[12]  M. de Rijke,et al.  Multileaved Comparisons for Fast Online Evaluation , 2014, CIKM.

[13]  Katja Hofmann,et al.  Lerot: an online learning to rank framework , 2013, LivingLab '13.

[14]  Wei Gao,et al.  Joint Ranking for Multilingual Web Search , 2009, ECIR.

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

[16]  Katja Hofmann,et al.  Online Learning to Rank: Absolute vs. Relative , 2015, WWW.

[17]  M. de Rijke,et al.  Optimizing Base Rankers Using Clicks - A Case Study Using BM25 , 2014, ECIR.

[18]  Thorsten Joachims,et al.  The K-armed Dueling Bandits Problem , 2012, COLT.

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

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

[21]  Massih-Reza Amini,et al.  Multiview Semi-supervised Learning for Ranking Multilingual Documents , 2011, ECML/PKDD.

[22]  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 .

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

[24]  Filip Radlinski,et al.  Query chains: learning to rank from implicit feedback , 2005, KDD '05.

[25]  Douglas W. Oard,et al.  Probabilistic structured query methods , 2003, SIGIR.