Semantic Web Dog Food

Presented at: 20th International World Wide Web Conference (WWW2011)

by Azin Ashkan, Charles L. A. Clarke

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.

On the Informativeness of Cascade and Intent-Aware Effectiveness Measures was presented at this event.

Keywords: Evaluation, World Wide Web

Resource URI on the dog food server: http://data.semanticweb.org/conference/www/2011/paper/on-the-informativeness-of-cascade-and-intent-aware

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