An Advanced User Intent Model Based On User Learning Process

User intent analysis is a continuous research hotspot in the field of query expansion. However, the big amount of irrelevant feedbacks in search log has negatively impacted the precision of user intent model. By observing the log, it can be found that tentative click is a major source of irrelevant feedback. It is also observed that a kind of new feedback information can be extracted from the log to recognize the characteristics of tentative clicks. With this new feedback information, this paper proposes an advanced user intent model and applies it into query expansion. Experiment results show that the model can effectively decrease the negative impact of irrelevant feedbacks that belong to tentative clicks and increase the precision of query expansion, especially for those informational queries.