- (Wang et al., 2009) ⇒ Xuanhui Wang, Deepayan Chakrabarti, and Kunal Punera. (2009). “Mining Broad Latent Query Aspects from Search Sessions.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557114
- Categories and Subject Descriptors: H.3.3 Information Search and Retrieval: Search process, Query formulation.
- General Terms: Algorithms.
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 [math]k[/math] 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.
|2009 MiningBroadLatentQueryAspectsfr||Xuanhui Wang|
|Mining Broad Latent Query Aspects from Search Sessions||KDD-2009 Proceedings||10.1145/1557019.1557114||2009|
|Author||Xuanhui Wang +, Deepayan Chakrabarti + and Kunal Punera +|
|journal||Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining +|
|title||Mining Broad Latent Query Aspects from Search Sessions +|