- (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
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.
|2008 BypassRatesReducingQueryAbandon||Atish Das Sarma|
|Bypass Rates: Reducing Query Abandonment Using Negative Inferences||KDD-2008 Proceedings||10.1145/1401890.1401916||2008|
|Author||Atish Das Sarma +, Sreenivas Gollapudi + and Samuel Ieong +|
|journal||Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining +|
|title||Bypass Rates: Reducing Query Abandonment Using Negative Inferences +|