A Context-Aware Recommender System for Ubiquitous Computing Environment : CARS
Do Hyun Ahn, Jae Kyeong Kim · 2005
Recommender systems have been widely advocated as a way of coping with the problem of information overload in e-business environment. Most of the existing recommender systems focused on what kind of items to recommend, although when to recommend to the target customer considering their context is an important issue. Even right item might be a spam advertisement or wrong recommendation for the customer if it can not be recommended at the right context. It is particularly important for recommendations where the user's context is changing rapidly, such as in both handheld and ubiquitous computing environment. Therefore, we propose CARS (Context-Aware Recommender System) based on CBR and context-awareness for ubiquitous computing environment. CBR is used to generate a target customer class and proper context. Context-awareness is used to gather user context information from sensors, networks, device status, user profiles, and other sources. An illustrative case example is suggested to explain the procedure of CARS.