document: Mining broad latent query aspects from search sessions 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 .
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