Paper Info
Title
Evaluating diversified search results using per-intent graded relevance
Abstract
Search queries are often ambiguous and/or underspecified. To accomodate different user needs, search result diversification has received attention in the past few years. Accordingly, several new metrics for evaluating diversification have been proposed, but their properties are little understood. We compare the properties of existing metrics given the premises that (1) queries may have multiple intents; (2) the likelihood of each intent given a query is available; and (3) graded relevance assessments are available for each intent. We compare a wide range of traditional and diversified IR metrics after adding graded relevance assessments to the TREC 2009 Web track diversity task test collection which originally had binary relevance assessments. Our primary criterion is discriminative power, which represents the reliability of a metric in an experiment. Our results show that diversified IR experiments with a given number of topics can be as reliable as traditional IR experiments with the same number of topics, provided that the right metrics are used. Moreover, we compare the intuitiveness of diversified IR metrics by closely examining the actual ranked lists from TREC. We show that a family of metrics called D#-measures have several advantages over other metrics such as α-nDCG and Intent-Aware metrics.
Year
DOI
Venue
2011
10.1145/2009916.2010055
SIGIR
Keywords
Field
DocType
graded relevance assessment,diversified ir experiment,traditional ir experiment,intent-aware metrics,per-intent graded relevance,right metrics,new metrics,search query,diversified ir metrics,multiple intent,binary relevance assessment,diversified search result,evaluation
Data mining,Ranking,Information retrieval,Computer science,Diversification (marketing strategy),Ambiguity,Discriminative model,Binary number
Conference
Citations 
PageRank 
References 
83
2.89
30
Authors
2
Name
Order
Citations
PageRank
Tetsuya Sakai11460139.97
Ruihua Song2113859.33