Intent based relevance estimation from click logs
Estimating the relevance of documents based on the user feedback is an essential component of search, retrieval and ranking problems. User click modeling in search has focused primarily on factoring out the position bias. It is easy to see that the query type (generic queries vs specific queries) and user intent (purchase vs exploration) also introduce a bias in the click signal. In other words, the results not matching with the user intent will not be clicked. In this paper, we outline a technique to model the interplay of query, user intent and position bias with respect to the relevance of the retrieved search results. In particular, we define two intents namely purchase and explore, and estimate the relevance of the documents with respect to these two intents. We also relate them to the relevance estimates from considering only the position bias. We empirically demonstrate the effectiveness of the proposed approach by comparing its performance against the well-known CoEC measure and the recently proposed factor model approach for relevance estimation.