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