An in-depth study on diversity evaluation: The importance of intrinsic diversity

Diversified document ranking has been recognized as an effective strategy to tackle ambiguous and/or underspecified queries. In this paper, we conduct an in-depth study on diversity evaluation that provides insights for assessing the performance of a diversified retrieval system. By casting the widely used diversity metrics (e.g., ERR-IA, α-nDCG and D#-nDCG) into a unified framework based on marginal utility, we analyze how these metrics capture extrinsic diversity and intrinsic diversity. Our analyses show that the prior metrics (ERR-IA, α-nDCG and D#-nDCG) are not able to precisely measure intrinsic diversity if we merely feed a set of subtopics into them in a traditional manner (i.e., without fine-grained relevance knowledge per subtopic). As the redundancy of relevant documents with respect to each specific information need (i.e., subtopic) can not be then detected and solved, the overall diversity evaluation may not be reliable. Furthermore, a series of experiments are conducted on a gold standard collection (English and Chinese) and a set of submitted runs, where the intent-square metrics that extend the diversity metrics through incorporating hierarchical subtopics are used as references. The experimental results show that the intent-square metrics disagree with the diversity metrics (ERR-IA and α-nDCG) being used in a traditional way on top-ranked runs, and that the average precision correlation scores between intent-square metrics and the prior diversity metrics (ERR-IA and α-nDCG) are fairly low. These results justify our analyses, and uncover the previously-unknown importance of intrinsic diversity to the overall diversity evaluation.