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Mining Broad Latent Query Aspects from Search Sessions (2009)
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| Venue: | KDD '09 |
| Citations: | 14 - 1 self |
BibTeX
@MISC{Wang09miningbroad,
author = {Xuanhui Wang and Deepayan Chakrabarti and Kunal Punera},
title = {Mining Broad Latent Query Aspects from Search Sessions},
year = {2009}
}
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Abstract
Search queries are typically very short, which means they are often underspecified or have senses that the user did not think of. A broad latent query aspect is a set of keywords that succinctly represents one particular sense, or one particular information need, that can aid users in reformulating such queries. We extract such broad latent aspects from query reformulations found in historical search session logs. We propose a framework under which the problem of extracting such broad latent aspects reduces to that of optimizing a formal objective function under constraints on the total number of aspects the system can store, and the number of aspects that can be shown in response to any given query. We present algorithms to find a good set of aspects, and also to pick the best k aspects matching any query. Empirical results on real-world search engine logs show significant gains over a strong baseline that uses single-keyword reformulations: a gain of 14 % and 23 % in terms of human-judged accuracy and click-through data respectively, and around 20 % in terms of consistency among aspects predicted for “similar ” queries. This demonstrates both the importance of broad query aspects, and the efficacy of our algorithms for extracting them.
Keyphrases
broad latent query aspect search session broad latent aspect formal objective function single-keyword reformulations similar query particular sense particular information need good set click-through data empirical result query reformulations present algorithm total number real-world search engine log strong baseline broad query aspect human-judged accuracy search query significant gain historical search session log
