This paper explores log-based query expansion ( QE ) models for Web search . Three lexicon models are proposed to bridge the lexical gap between Web documents and user queries . These models are trained on pairs of user queries and titles of clicked documents . Evaluations on a real world data set show that the lexicon models , integrated into a ranker-based QE system , not only significantly improve the document retrieval performance but also outperform two state-of-the-art log-based QE methods . Term mismatch is a fundamental problem in Web search , where queries and documents are composed using different vocabularies and language styles . Query expansion ( QE ) is an effective strategy to address the problem . It expands a query issued by a user with additional related terms , called expansion terms , so that more relevant documents can be retrieved . In this paper we explore the use of clickthrough data and translation models for QE . We select expansion terms for a query according to how likely it is that the expansion terms occur in the title of a document that is relevant to the query . Assuming that a query is parallel to the titles of documents clicked for that query ( Gao et al . 2010a ) , three lexicon models are trained on query-title pairs extracted from clickthrough data . The first is a word model that learns the translation probability between single words . The second model uses lexi-calized triplets to incorporate word dependencies for translation . The third is a bilingual topic model , which represents a query as a distribution of hidden topics and learns the translation between a query and a title term at the semantic level . We will show that the word model provides a rich set of expansion candidates while the triplet and topic models can effectively select good expansion terms , and that a ranker-based QE system which incorporates all three of these models not only significantly improves Web search result but outperforms other log-based QE methods that are stateof-the-art . There is growing interest in applying user logs to improve QE . A recent survey is due to Baeze-Yates and Ribeiro-Neto ( 2011 ) . Below , we briefly discuss two log-based QE methods that are closest to ours and are re-implemented in this study for comparison . Both systems use the same type of log data that we used to train the lexicon models . The term correlation model of Cui et al . ( 2002 ; 2003 ) is to our knowledge the first to explore querydocument relations for direct extraction of expansion terms for Web search . The method outperforms traditional QE methods that do not use log data e.g. the local analysis model of Xu and Croft ( 1996 ) . In addition , as pointed out by Cui et al . ( 2003 ) there are three important advantages that make log-based QE a promising technology to improve the performance of commercial search engines . First , unlike traditional QE methods that are based on relevance feedback , log-based QE derives expansion terms from search logs , allowing term correlations to be pre-computed offline . Compared to methods that are based on thesauri either compiled manually ( Prager et al . 2001 ) or derived au-tomatically from document collections ( Jing and Croft 1994 ) , the log-based method is superior in that it explicitly captures the correlation between query terms and document terms , and thus can bridge the lexical gap between them more effectively . Second , since search logs retrain querydocument pairs clicked by millions of users , the term correlations reflect the preference of the majority of users . Third , the term correlations evolve along with the accumulation of user logs , thus can reflect updated user interests at a specific time . However , as pointed out by Riezler et al . ( 2008 ) , Cui et al . 's correlation-based method suffers low precision of QE partly because the correlation model does not explicitly capture context information and is susceptible to noise . Riezler et al . developed a QE system by retraining a standard phrase-based statistical machine translation ( SMT ) system using query-snippet pairs extracted from clickthrough data ( Riezler et al . 2008 ; Riezler and Liu 2010 ) . The SMT-based system can produce cleaner , more relevant expansion terms because rich context information useful for filtering noisy expansions is captured by combining language model and phrase translation model in its decoder . Furthermore , in the SMT system all component models are properly smoothed using sophisticated techniques to avoid sparse data problems while the correlation model relies on pure counts of term frequencies . However , the SMT system is used as a black box in their experiments . So the relative contribution of different SMT components is not verified empirically . In this study we break this black box in order to build a better , simpler QE system . We will show that the proposed lexicon models outperform significantly the term correlation model , and that a simpler QE system that incorporates the lexicon models can beat the sophisticated , black-box SMT system . In this paper we extend the previous log-based QE methods in two directions . First , we formulate QE as the problem of translating a source language of queries into a target language of documents , represented as titles . This allows us to adapt the established techniques developed for SMT to QE . Specially , we propose three lexicon models based on terms , lexicalized triplets , and topics , respectively . These models are trained on pairs of user queries and the titles of clicked documents using EM . Second , we present a ranker-based QE system , the heart of which is a MRF-based ranker in which the lexicon models are incorporated as features . We perform experiments on the Web search task using a real world data set . Results show that the proposed system outperforms significantly other stateof-the-art QE systems . This study is part of a bigger , ongoing project , aiming to develop a real-time QE system for Web search , where simplicity is the key to the success . Thus , what we learned from this study is particularly encouraging . We demonstrate that with large amounts of clickthrough data for model training , simple lexicon models can achieve state-of-the-art QE performance , and that the MRF-based ranker provides a simple and flexible framework to incorporate a variety of features capturing different types of term dependencies in such an effective way that the Web search performance can be directly optimized .

Challenge: Term mismatches between a query and documents hinder retrievals of relevant documents and black box statistical machine translation models are used to expand queries.
Approach: They propose to train lexicon query expansion models by using transaction logs that contain pairs of queries and titles of clicked documents.
Outcome: The proposed query expansion model enables retrieval systems to significantly outperform models with previous expansion models while being more transparent.