Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Query word deletion prediction
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Mining anchor text for query refinement
Proceedings of the 13th international conference on World Wide Web
Simple BM25 extension to multiple weighted fields
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Scoring missing terms in information retrieval tasks
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Modeling search engine effectiveness for federated search
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
Regularized estimation of mixture models for robust pseudo-relevance feedback
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
On ranking the effectiveness of searches
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Precision prediction based on ranked list coherence
Information Retrieval
Random walks on the click graph
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Performance prediction using spatial autocorrelation
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Adaptive mixtures of local experts
Neural Computation
A unified and discriminative model for query refinement
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Mining term association patterns from search logs for effective query reformulation
Proceedings of the 17th ACM conference on Information and knowledge management
Query Expansion Using External Evidence
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
On the local optimality of LambdaRank
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Reducing long queries using query quality predictors
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Query reformulation using anchor text
Proceedings of the third ACM international conference on Web search and data mining
Exploring reductions for long web queries
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Predicting query performance on the web
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Learning to rank query reformulations
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Improving verbose queries using subset distribution
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Cluster-based fusion of retrieved lists
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Semi-supervised learning to rank with preference regularization
Proceedings of the 20th ACM international conference on Information and knowledge management
Machine learning for query-document matching in search
Proceedings of the fifth ACM international conference on Web search and data mining
Beyond bag-of-words: machine learning for query-document matching in web search
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Query performance prediction for IR
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Exploiting External Collections for Query Expansion
ACM Transactions on the Web (TWEB)
Predicting query performance for fusion-based retrieval
Proceedings of the 21st ACM international conference on Information and knowledge management
Collaborative ranking: improving the relevance for tail queries
Proceedings of the 21st ACM international conference on Information and knowledge management
Modeling reformulation using query distributions
ACM Transactions on Information Systems (TOIS)
Example based entity search in the web of data
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Late data fusion for microblog search
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Learning to rank query suggestions for adhoc and diversity search
Information Retrieval
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Search engines can automatically reformulate user queries in a variety of ways, often leading to multiple queries that are candidates to replace the original. However, selecting a replacement can be risky: a reformulation may be more effective than the original or significantly worse, depending on the nature of the query, the source of reformulation candidates, and the corpus. In this paper, we explore methods to mitigate this risk by issuing several versions of the query (including the original) and merging their results. We focus on reformulations generated by random walks on the click graph, a method that can produce very good reformulations but is also variable and prone to topic drift. Our primary contribution is λ-Merge, a supervised merging method that is trained to directly optimize a retrieval metric (such as NDCG or MAP) using features that describe both the reformulations and the documents they return. In experiments on Bing data and GOV2, λ-Merge outperforms the original query and several unsupervised merging methods. λ-Merge also outperforms a supervised method to predict and select the best single formulation, and is competitive with an oracle that always selects the best formulation.