Learning the Popularity of Items for Mobile Tourist Guides

A context-aware recommender system incorporates the knowledge of different contextual factors such as time or weather information to improve item suggestions made to a user. This requires the system to have a large knowledge base for inferring contextual information and enabling accurate and timely recommendations. We present a versatile approach for a context-aware recommender system in the tourism domain by crawling publicly available information from a variety of sources and learning the contextual popularity of points of interest based on a generalized check-in model. We have deployed a test instance of our system for the greater area of Munich and the German state of Bavaria. Analyzing the results from the offline learning has led to interesting insights including when and in which weather conditions certain items are popular.