MM: A new Framework for Multidimensional Evaluation of Search Engines
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Abstract
In this paper, we proposed a framework to evaluate information retrieval systems in presence of multidimensional relevance. This is an important problem in tasks such as consumer health search, where the understandability and trustworthiness of information greatly influence people’s decisions based on the search engine results, but common topicality-only evaluation measures ignore these aspects. We used synthetic and real data to compare our proposed framework, named MM, to the understandability-biased information evaluation (UBIRE), an existing framework used in the context of consumer health search. We showed how the proposed approach diverges from the UBIRE framework, and how MM can be used to better understand the trade-offs between topical relevance and the other relevance dimensions.