document: Bypass rates : reducing query abandonment using negative inferences 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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