Deep Sequential Models for Task Satisfaction Prediction
Rishabh Mehrotra, Ahmed Hassan Awadallah, Milad Shokouhi, Emine Yılmaz, Imed Zitouni, Ahmed El Kholy, Madian Khabsa · 2017
Detecting and understanding implicit signals of user satisfaction are essential for experimentation aimed at predicting searcher satisfaction. As retrieval systems have advanced, search tasks have steadily emerged as accurate units not only to capture searcher's goals but also in understanding how well a system is able to help the user achieve that goal. However, a major portion of existing work on modeling searcher satisfaction has focused on query level satisfaction. The few existing approaches for task satisfaction prediction have narrowly focused on simple tasks aimed at solving atomic information needs.