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This paper presents a novel document relevance model based on clickthrough information. Compared to the models from the literature we consider the case when documents can be clicked several times in a given search session. This case occurs more and more frequently, specifically for multi-clickable documents such as maps in location search enginess. Considering a real system query log, we evaluate our model and show that SVM can learn with fewer errors and with better MAP when the various types of clicks are considered in the model.

Multiple Clicks Model for Web Search of Multi-clickable Documents