Overview of the Special Issue on Contextual Search and Recommendation - Bennett, Paul; Collins-Thompson, Kevyn; Kelly, Diane; White, Ryen W.; Zhang, Yi (ACM Transactions on Information Systems, 2015)
Information systems that leverage contextual knowledge about their users and their search situations – such as 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 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