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Data-driven Serendipity Navigation in Urban Places

DocUID: 2017-004 Full Text: PDF

Author: Xiaoyu Ge, Ameya Daphalapurkar, Manali Shimpi, Darpun Kohli, Kostas Pelechrinis, Panos K. Chrysanthis, Demetrios Zeinalipour-Yazti

Abstract: With the proliferation of mobile computing and the ability to collect detailed data for the urban environment a number of systems that aim at providing Points of Interest (POIs) and tour recommendations have appeared. The overwhelming majority of these systems aims at providing an optimal recommendation, where optimality refers to objectives of minimizing the distance to be covered or maximizing the quality of the POIs recommended. A major problem is that by focusing on the optimization of these objectives, there remains little room to the user for serendipity. Urban and social scientists have identified serendipity, i.e., the ability to come across unexpected places, as a feature that makes a city livable. In this work, we introduce a prototype of an experimental platform for evaluating venue recommendation algorithms by providing informative tour recommendations based on the suggested venues. Our prototype system integrates the notion of serendipity in urban navigation at both the venue as well as the route recommendation level without compromising the quality and diversity of the recommended POIs. In addition, our system allows the user to upload their own algorithms and explore their performance as compared to many well-known algorithms.

Keywords: Preferential Diversity, Recommendation Systems, Big Data, Data Exploration

Published In: The 37th IEEE International Conference on Distributed Computing Systems

Pages: 1-4

Place Published: Atlanta, GA, USA

Year Published: 2017

Project: Serendipity Navigation,  Mobile Personal Guide (MPG) Subject Area: Urban Informatics

Publication Type: Demonstration

Sponsor: Others, RDF

Citation:Text Latex BibTex XML Xiaoyu Ge, Ameya Daphalapurkar, Manali Shimpi, Darpun Kohli, Kostas Pelechrinis, Panos K. Chrysanthis, and Demetrios Zeinalipour-Yazti. Data-driven Serendipity Navigation in Urban Places. The 37th IEEE International Conference on Distributed Computing Systems. 1-4. 2017. Atlanta, GA, USA.