In Search for Relevant, Diverse and Crowd-screen Points of Interests
DocUID: 2017-001 Full Text:
PDF Author: Xiaoyu Ge, Samanvoy Reddy Panati, Kostas Pelechrinis, Panos K. Chrysanthis, Mohamed A. Sharaf
Abstract: In this demo we present a prototype of an experimental platform for evaluating item recommendation algorithms. The application domain 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.
Keywords: Urban Informatics, Preferential Diversity, Recommendation System
Published In: Proceedings of the 20th International Conference on Extending Database Technology
ISBN: 978-3-89318-073-8
Pages: 578-581
Place Published: Venice, Italy
Year Published: 2017
Project: Mobile Personal Guide (MPG) Subject Area: Urban Informatics, Data Exploration, Data Personalization
Publication Type: Demonstration
Sponsor: Others