Mobile Recommendations for Leisure Activities

We demonstrate a context-aware mobile system for recommending information about leisure activities (Shopping, Eating, Doing, Seeing, and Reading), codenamed Magitti, which infers the user’s leisure activity from context and patterns of behavior. Magitti filters a database of city-guide-style leisure information to find the most relevant items based on the user’s profile, history, context, and predicted activity. Users can also customize the profile or dynamically adjust the current preferences if they wish to improve the recommendations further. Author Keywords context-aware, mobile recommendation systems