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.
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