Learning Consensus Opinion: Mining Data from a Labeling Game

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Abstract

We consider the problem of identifying the consensus rank- ing for the results of a query, given preferences among those results from a set of individual users. Once consensus rank- ings are identified for a set of queries, these rankings can serve for both evaluation and training of retrieval and learn- ing systems. We present a novel approach to collecting the individual user preferences over image-search results: we use a collaborative game in which players are rewarded for agree- ing on which image result is best for a query. Our approach is distinct from other labeling games because we are able to elicit directly the preferences of interest with respect to image queries extracted from query logs. As a source of rel- evance judgments, this data provides a useful complement to click data. Furthermore, the data is free of positional biases and is collected by the game without the risk of frus- trating users with non-relevant results; this risk is prevalent in standard mechanisms for debiasing clicks. We describe data collected over 34 days from a deployed version of this game that amounts to about 18 million expressed prefer- ences between pairs. Finally, we present several approaches to modeling this data in order to extract the consensus rank- ings from the preferences and better sort the search results for targeted queries.