User intent and assessor disagreement in web search evaluation

Abstract

Preference based methods for collecting relevance data for information retrieval (IR) evaluation have been shown to lead to better inter-assessor agreement than the traditional method of judging individual documents. However, little is known as to why preference judging reduces assessor disagreement and whether better agreement among assessors also means better agreement with user satisfaction, as signaled by user clicks. In this paper, we examine the relationship between assessor disagreement and various click based measures, such as click preference strength and user intent similarity, for judgments collected from editorial judges and crowd workers using single absolute, pairwise absolute and pairwise preference based judging methods. We find that trained judges are significantly more likely to agree with each other and with users than crowd workers, but inter-assessor agreement does not mean agreement with users. Switching to a pairwise judging mode improves crowdsourcing quality close to that of trained judges. We also find a relationship between intent similarity and assessor-user agreement, where the nature of the relationship changes across judging modes. Overall, our findings suggest that the awareness of different possible intents, enabled by pairwise judging, is a key reason of the improved agreement, and a crucial requirement when crowdsourcing relevance data.