document: On the informativeness of cascade and intent-aware effectiveness measures The Maximum Entropy Method provides one technique for validating search engine effectiveness measures . Under this method , the value of an effectiveness measure is used as a constraint to estimate the most likely distribution of relevant documents under a maximum entropy assumption . This inferred distribution may then be compared to the actual distribution to quantify the `` informativeness '' of the measure . The inferred distribution may also be used to estimate values for other effectiveness measures . Previous work focused on traditional effectiveness measures , such as average precision . In this paper , we extend the Maximum Entropy Method to the newer cascade and intent-aware effectiveness measures by considering the dependency of the documents ranked in a results list . These measures are intended to reflect the novelty and diversity of search results in addition to the traditional relevance . Our results indicate that intent-aware measures based on the cascade model are informative in terms of both inferring actual distribution and predicting the values of other retrieval measures .
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