Special Issue of ACM TOIS on Contextual Search and Recommendation

Information systems that leverage contextual knowledge about their users and their search situations, such as histories, demographics, surroundings, constraints, or devices,canprovidetailoredsearchexperiencesandhigher-qualitytaskoutcomes.Within information retrieval, there is a growing focus on how knowledge of user 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 recent growth in work on complex task-oriented search and recommendation combined with the interest in context derived from mobile and situated devices—and across devices—make this an opportune time for a special issue in this area. Given the timeliness and breadth of the topic, as well as the level of interest in events such as related workshops, we believe that readers will find these articles both informative and thought provoking.

Guest Editors
Paul N. Bennett
Kevyn Collins-Thompson
Diane Kelly
Ryen W. White
Yi Zhang