Personalized Web search has emerged as one of the hottest topics for both the Web industry and academic researchers. However, the majority of studies on personalized search focused on a rather simple type of search, which leaves an important research topic - the personalization in exploratory searches - as an under-studied area. In this paper, we present a study of personalization in task-based information exploration using a system called TaskSieve. TaskSieve is a Web search system that utilizes a relevance feedback based profile, called a "task model", for personalization. Its innovations include flexible and user controlled integration of queries and task models, task-infused text snippet generation, and on-screen visualization of task models. Through an empirical study using human subjects conducting task-based exploration searches, we demonstrate that TaskSieve pushes significantly more relevant documents to the top of search result lists as compared to a traditional search system. TaskSieve helps users select significantly more accurate information for their tasks, allows the users to do so with higher productivity, and is viewed more favorably by subjects under several usability related characteristics.

In profile-based or content-based adaptive systems, one of the open research questions is how frequently the user's profile and the list of recommended items should be updated. Different systems tend to choose one of the two extremes. Some systems do it once per session (thus called between-session update strategy), whereas some others update whenever there is feedback (called instant update strategy). This paper presents our attempt to assess the value of keeping the list of recommended items up-to-date in the context of task-based information exploration. We conducted controlled studies involving human users performing realistic tasks using two systems that have the same adaptive filtering engine but with the above two different update strategies. Our results show that the between-session strategy helped to find better quality information, and received better subjects' responses about its usefulness and usability. However, it prolonged the selection of useful passages, whereas the instant update strategy helped subjects to obtain almost all of their selected passages (>98%) within the first 5 minutes. Based on the results, we hypothesize that the best strategy for updating might be a hybrid between the two update strategies, where both adaptability and stability can be achieved.

Over the last five years, a range of projects have focused on progressively more elaborated techniques for adaptive news delivery. However, the adaptation process in these systems has become more complicated and thus less transparent to the users. In this paper, we concentrate on the application of open user models in adding transparency and controllability to adaptive news systems. We present a personalized news system, YourNews, which allows users to view and edit their interest profiles, and report a user study on the system. Our results confirm that users prefer transparency and control in their systems, and generate more trust to such systems. However, similar to previous studies, our study demonstrate that this ability to edit user profiles may also harm the system.s performance and has to be used with caution.