Detecting intent shift is fundamental for learning users’ behaviors and applying their experiences. In this paper, we propose a search-query-log based system to predict users’ intent shifts. We begin with selecting sessions in search query logs for training, extracting features from the selected sessions, and clustering sessions of similar intent. The resulting intent clusters are used to predict intent shift in testing data. The experimental results show that the proposed model achieves an
accuracy of 0.5099, which is significantly better than the baselines. Moreover, the miss rate and spurious rate of the model are 0.0954 and 0.0867, respectively.