2008 BypassRatesReducingQueryAbandon

• (Das Sarma et al., 2008) ⇒ Atish Das Sarma, Sreenivas Gollapudi, and Samuel Ieong. (2008). “Bypass Rates: Reducing Query Abandonment Using Negative Inferences.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401916

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%22Bypass+rates%3A+reducing+query+abandonment+using+negative+inferences%22+2008
• http://portal.acm.org/citation.cfm?doid=1401890.1401916&preflayout=flat#citedby

Quotes

Abstract

We introduce a new approach to analyzing click logs by examining both the documents that are clicked and those that are bypassed-documents returned higher in the ordering of the search results but skipped by the user. This approach complements the popular click-through rate analysis, and helps to draw negative inferences in the click logs. We formulate a natural objective that finds sets of results that are unlikely to be collectively bypassed by a typical user. This is closely related to the problem of reducing query abandonment. We analyze a greedy approach to optimizing this objective, and establish theoretical guarantees of its performance. We evaluate our approach on a large set of queries, and demonstrate that it compares favorably to the maximal marginal relevance approach on a number of metrics including mean average precision and mean reciprocal rank.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 BypassRatesReducingQueryAbandon	Atish Das Sarma Sreenivas Gollapudi Samuel Ieong			Bypass Rates: Reducing Query Abandonment Using Negative Inferences				10.1145/1401890.1401916