Modeling Users´ Information Goal Transitions and Satisfaction Judgment: Understanding the Full Search Process

To improve web search effectiveness and help personalized search applications, it is important to understand users´ search process, especially the underlying information goal transitions and satisfaction judgment on result pages. Unlike previous work modeling the two types of hidden information separately, the paper proposes to simultaneously model them based on users´ full search process, including both queries and clicks. Thus, a full model can be built up and the dependences between them can be leveraged. Specially, we employ a hierarchical conditional random field (HCRF) for learning and prediction, with fruitful search activity features proposed and leveraged. Experimental results show that our approach reaches a high overall precision(87%) and significantly outperforms the baseline methods. Moreover, our model is applied in a re-ranking application and shows that it can benefit personalized web search.