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 c...

• Type: Conference
• Year: 2009
• Where: WWW
• Authors: Paul N. Bennett, David Maxwell Chickering, Anton Mityagin
• Keywords: Consensus Rankings, Individual User Preferences, Internet Technology, Keywords Preference Judgments, WWW 2009