Personalization of search results using interaction behaviors in search sessions

Chang Liu, Nicholas J. Belkin, Michael J. Cole
2012 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval - SIGIR '12  
Personalization of search results offers the potential for significant improvement in information retrieval performance. User interactions with the system and documents during informationseeking sessions provide a wealth of information about user preferences and their task goals. In this paper, we propose methods for analyzing and modeling user search behavior in search sessions to predict document usefulness and then using information to personalize search results. We generate prediction
more » ... of document usefulness from behavior data collected in a controlled lab experiment with 32 participants, each completing uncontrolled searching for 4 tasks in the Web. The generated models are then tested with another data set of user search sessions in radically different search tasks and constrains. The documents predicted useful and not useful by the models are used to modify the queries in each search session using a standard relevance feedback technique. The results show that application of the models led to consistently improved performance over a baseline that did not take account of user interaction information. These findings have implications for designing systems for personalized search and improving user search experience.
doi:10.1145/2348283.2348314 dblp:conf/sigir/LiuBC12 fatcat:jfo7wtrg5vg3lp7iv5exee67je