Incorporating Position Bias into Click-Through Bipartite Graph

Click-through bipartite graph has been regarded as an effective method in search user behavior analysis researches. In most existing bipartite graph construction studies, user clicks are treated as equally important. However, considering the existence of position bias factor in user click-through behavior, clicks on results in different ranking positions should be treated separately. In this work, we choose a classical click-through bipartite graph model, which named label propagation model, and evaluate the improvement of performance by considering the effect of position bias. We propose three hypotheses to explain the influence of position bias, and modify the formulas of label propagation algorithm. We use AUC as the evaluation metric, which express the effectiveness of spam URLs identification by label propagation algorithm and its improved methods. The experimental results demonstrate that the proposed methods work better than the baseline method.