Learning Consensus Opinion: Mining Data from a Labeling Game

We consider the problem of identifying the consensus ranking for the results of a query, given preferences among those results from a set of individual users. Once consensus rankings are identified for a set of queries, these rankings can serve for both evaluation and training of retrieval and learning 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 agreeing 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 relevance 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 frustrating 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 preferences between pairs. Finally, we present several approaches to modeling this data in order to extract the consensus rankings from the preferences and better sort the search results for targeted queries.