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Query-Performance Prediction Using Minimal Relevance Feedback

There has been much work on devising query-performance prediction approaches that estimate search effectiveness without relevance judgments (i.e., zero feedback). Specifically, post-retrieval predictors analyze the result list of top-retrieved documents. Departing from the zero-feedback approach, in this paper we show that relevance feedback for even very few top ranked documents can be exploited to dramatically improve prediction quality. Specifically, applying state-of-the-art zero-feedback-based predictors to only a very few relevant documents, rather than to the entire result list as originally designed, substantially improves prediction quality. This novel form of prediction is based on quantifying properties of relevant documents that can attest to query performance. We also show that integrating prediction based on relevant documents with zero-feedback-based prediction is highly effective; specifically, with respect to utilizing state-of-the-art direct estimates of retrieval effectiveness when minimal feedback is available.