The electrical resistance of a graph captures its commute and cover times
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Beyond independent relevance: methods and evaluation metrics for subtopic retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A risk minimization framework for information retrieval
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Less is more: probabilistic models for retrieving fewer relevant documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Knowledge and Data Engineering
Random walks on the click graph
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Active exploration for learning rankings from clickthrough data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning diverse rankings with multi-armed bandits
Proceedings of the 25th international conference on Machine learning
Minimally invasive randomization for collecting unbiased preferences from clickthrough logs
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
An axiomatic approach for result diversification
Proceedings of the 18th international conference on World wide web
PSkip: estimating relevance ranking quality from web search clickthrough data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Large-scale uncertainty management systems: learning and exploiting your data
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Good abandonment in mobile and PC internet search
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Addressing people's information needs directly in a web search result page
Proceedings of the 20th international conference on World wide web
Diversity in ranking via resistive graph centers
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Intent-based diversification of web search results: metrics and algorithms
Information Retrieval
Interpreting user inactivity on search results
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Reranking web search results for diversity
Information Retrieval
Leaving so soon?: understanding and predicting web search abandonment rationales
Proceedings of the 21st ACM international conference on Information and knowledge management
The last click: why users give up information network navigation
Proceedings of the 7th ACM international conference on Web search and data mining
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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.