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. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: Retrieval Models General Terms Algorithms, Exper...

• Type: Conference
• Year: 2008
• Where: KDD
• Authors: Atish Das Sarma, Sreenivas Gollapudi, Samuel Ieong
• Keywords: Data Mining, Greedy Approach, KDD 2008, Keywords Query Abandonment, Marginal Relevance Approach