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Batch evaluation with test collections of documents, search topics, and relevance judgments has been the bedrock of IR evaluation since its adoption by Salton for his experiments on vector space systems. Such test collections have limitations: they contain no user interaction data; there is typically only one query per topic; they have limited size due to the cost of constructing them. In the last 15-20 years, it has become evident that having a log of user interactions and a large space of queries is invaluable for building effective retrieval systems, but such data is generally only available to search engine companies. Thus there is a gap between what academics can study using static test collections and what industrial researchers can study using dynamic user data.
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Carterette et al. (2015) studied this question.
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