Smoothing Click Counts for Aggregated Vertical Search

Clickthrough data is a critical feature for improving web search ranking. Recently, many search portals have provided aggregated search, which retrieves relevant information from various heterogeneous collections called verticals. In addition to the well-known problem of rank bias, clickthrough data recorded in the aggregated search environment suffers from severe sparseness problems due to the limited number of results presented for each vertical. This skew in clickthrough data, which we call rank cut, makes optimization of vertical searches more difficult. In this work, we focus on mitigating the negative effect of rank cut for aggregated vertical searches. We introduce a technique for smoothing click counts based on spectral graph analysis. Using real clickthrough data from a vertical recorded in an aggregated search environment, we show empirically that clickthrough data smoothed by this technique is effective for improving the vertical search