Task-Oriented Web User Modeling for Recommendation

Abstract. We propose an approach for modeling the navigational behavior of Web users based on task-level patterns. The discovered “tasks” are characterized probabilistically as latent variables, and represent the underlying interests or intended navigational goal of users. The ability to measure the probabilities by which pages in user sessions are associated with various tasks, allow us to track task transitions and modality shifts within (or across) user sessions, and to generate task-level navigational patterns. We also propose a maximum entropy recommendation system which combines the page-level statistics about users’ navigational activities together with our task-level usage patterns. Our experiments show that the task-level patterns provide better interpretability of Web users’ navigation, and improve the accuracy of recommendations.

• Type: Conference
• Year: 2005
• Where: UM
• Authors: Xin Jin, Yanzan Zhou, Bamshad Mobasher
• Keywords: Navigational, Task-level Patterns, UM 2005, Web Users