|Congratulations to Xiaoyu and Samanvoy for their EDBT 2017 Demonstration: In Search for Relevant, Diverse and Crowdscreen Points of Interests|
Authors Xiaoyu Ge, Samanvoy Panati, Kostas Pelechrinis and Panos K. Chrysanthis
In this demo we present a prototype of an experimental platform for evaluating item recommendation algorithms. The application do- main for our system is that of digital city guides. Our prototype implementation allows the user to explore different algorithms and compare their output. Among the algorithms implemented is MPG, which aims at providing a diverse set of recommendations better aligned with user preferences. MPG takes into consideration the user preferences (e.g., reach willing to cover, types of venues interested in exploring etc.), the popularity of the establishments as well as their distance from the current location of the user by combining them into a single composite score. We provide a web interface, which outputs on a map the recommended locations along with metadata (e.g., type and name of location, relevance and diversity scores etc.). It also illustrates the potential of the Preferential Diversity approach on which MPG is based.