Expected reciprocal rank for graded relevance

  • Authors:

  • Olivier Chapelle;Donald Metlzer;Ya Zhang;Pierre Grinspan

  • Affiliations:

  • Yahoo! Labs, Santa Clara, CA, USA;Yahoo! Labs, Santa Clara, CA, USA;Yahoo! Labs, Sunnyvale, CA, USA;Google Inc, San Bruno, CA, USA

  • Venue:
  • Proceedings of the 18th ACM conference on Information and knowledge management
  • Year:
  • 2009

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Abstract

While numerous metrics for information retrieval are available in the case of binary relevance, there is only one commonly used metric for graded relevance, namely the Discounted Cumulative Gain (DCG). A drawback of DCG is its additive nature and the underlying independence assumption: a document in a given position has always the same gain and discount independently of the documents shown above it. Inspired by the "cascade" user model, we present a new editorial metric for graded relevance which overcomes this difficulty and implicitly discounts documents which are shown below very relevant documents. More precisely, this new metric is defined as the expected reciprocal length of time that the user will take to find a relevant document. This can be seen as an extension of the classical reciprocal rank to the graded relevance case and we call this metric Expected Reciprocal Rank (ERR). We conduct an extensive evaluation on the query logs of a commercial search engine and show that ERR correlates better with clicks metrics than other editorial metrics.