SIGIR '02 Proceedings of the 25th 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
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Exploring reductions for long web queries
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
LambdaMerge: merging the results of query reformulations
Proceedings of the fourth ACM international conference on Web search and data mining
Oracle in Image Search: A Content-Based Approach to Performance Prediction
ACM Transactions on Information Systems (TOIS)
Interactive search support for difficult web queries
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Adaptive query suggestion for difficult queries
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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
Session-based query performance prediction
Proceedings of the 21st ACM international conference on Information and knowledge management
Using document-quality measures to predict web-search effectiveness
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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Predicting the performance of web queries is useful for several applications such as automatic query reformulation and automatic spell correction. In the web environment, accurate performance prediction is challenging because measures such as clarity that work well on homogeneous TREC-like collections, are not as effective and are often expensive to compute. We present Rank-time Performance Prediction (RAPP), an effective and efficient approach for online performance prediction on the web. RAPP uses retrieval scores, and aggregates of the rank-time features used by the document- ranking algorithm to train regressors for query performance prediction. On a set of over 12,000 queries sampled from the query logs of a major search engine, RAPP achieves a linear correlation of 0.78 with DCG@5, and 0.52 with NDCG@5. Analysis of prediction accuracy shows that hard queries are easier to identify while easy queries are harder to identify.