Call for Papers
ACM Transactions on Information Systems
Special Issue on Contextual Search and Recommendation
Submission Deadline: March 24, 2014
MotivationInformation systems that leverage contextual knowledge about users and their search situations-such as their histories, demographics, surroundings, constraints or devices-can provide tailored search experiences and higher-quality task outcomes. Within information retrieval, there is a growing focus on how knowledge of a user's interests, intentions, and context can improve aspects of search and recommendation such as ranking and query suggestion; especially for exploratory and/or complex tasks that can span multiple queries or search sessions. The interactions that occur during these complex tasks provide context that can be leveraged by search systems to support users' broader information-seeking activities. Next-generation recommender systems face analogous challenges, including integrating signals from user exploration to update recommendations in real time.
The purpose of this special issue is to describe the state-of-the-art in contextual search and recommendation, and provide an outlet for the presentation of significant advances in these two important related areas.
Scope & Topics of InterestWhile all high-quality manuscripts focused on contextual search and recommendation will be considered, we are particularly interested in those approaches targeted at exploratory and/or complex tasks (referred to simply as "tasks" below for brevity). Topics of interest include, but are not limited to, the following:
- Task-oriented information search;
- Contextual retrieval for complex and exploratory tasks, including tasks that persist over time;
- Proactive search and recommendation of task-relevant content;
- Contextual recommendation systems for exploratory tasks, especially real-time updates and human-in-the-loop systems;
- Machine learning and data mining for task-oriented information search;
- Evaluating task-oriented information search;
- Task-awareness in mobile and situated devices, including personalization across devices and domains;
- User studies of task-oriented information search (in the lab and in the wild);
- Scalability issues in profile building and user privacy for task-oriented information search;
- Personalized task-oriented retrieval, including short- and long-term modeling.
Guest EditorsPaul N. Bennett, Microsoft Research (Primary Contact: email@example.com)
Kevyn Collins-Thompson, University of Michigan
Diane Kelly, University of North Carolina at Chapel Hill
Ryen White, Microsoft Research
Yi Zhang, University of California Santa Cruz
SubmissionFor submission instructions, please refer to http://tois.acm.org/authors.html. Add a comment in the email to the Assistant to the Editor-in-Chief that the submission is intended for the special issue on Contextual Search and Recommendation. All papers will be reviewed by three external reviewers plus at least one guest editor.
Last change: December 10, 2013