Workshop Program
| 09:00 | 09:10 | Opening Remarks |
| Session 1: Spatial Analytics | ||
| 09:10 | 09:25 | Classifying Points of Interest with Minimum Metadata |
| Giorgos Giannopoulos, Athena Research Center, Greece | ||
| Konstantinos Alexis, Athena Research Center, Greece | ||
| Nikos Kostagiolas, Athena Research Center, Greece | ||
| Dimitrios Skoutas, Athena Research Center, Greece | ||
| 09:25 | 09:55 | A Road Generalization Method using Layered Stroke Networks |
| Yonghwan Kim, Nagoya Institute of Technology, Japan | ||
| Hiroaki Fukuyasu, Nagoya Institute of Technology, Japan | ||
| Daisuke Yamamoto, Nagoya Institute of Technology, Japan | ||
| Naohisa Takahashi, Nagoya Institute of Technology, Japan | ||
| 09:55 | 10:40 | Keynote 1 - Spatial Analytics for storing and analyzing GeoSocial data for Geoadvertising |
| Siva Ravada, Oracle Corporation, USA | ||
| 10:40 | 11:00 | Coffee Break |
| Session 2: Recommendations | ||
| 11:00 | 11:30 | Intelligent sightseeing in immensely manifold cities: case of 2018 FIFA World Cup host cities |
| Ksenia Mukhina, ITMO University, Russia | ||
| Alexander Visheratin, ITMO University, Russia | ||
| Denis Nasonov, ITMO University, Russia | ||
| Lev Manovich, University of Tyumen, Russia | ||
| 11:30 | 11:45 | Inferring Ratings for Custom Trips from Rich GPS Traces |
| Theodoros Chondrogiannis, University of Konstanz, Germany | ||
| Mouzhi Ge, Masaryk University, Czech Republic | ||
| 11:45 | 12:00 | Emotion Predictions in Geo-Textual Data using Spatial Statistics and Recommendation Systems |
| Aisha Sikder, George Mason University, VA, USA | ||
| Andreas Züfle, George Mason University, VA, USA | ||
| 12:00 | 12:30 | Defining and Measuring Fairness in Location Recommendations |
| Leonard Weydemann, Travel Data Solution, Austria | ||
| Dimitris Sacharidis, Technische Universität Wien, Austria | ||
| Hannes Werthner, Technische Universität Wien, Austria | ||
| 12:30 | 14:00 | Lunch Break |
| Session 3: Privacy | ||
| 14:00 | 14:30 | Mobile Recommendation Challenges within a Strong Privacy Oriented Paradigm |
| Denys Proux, Naver Labs Europe, France | ||
| Frederic Roulland, Naver Labs Europe, France | ||
| 14:30 | 15:00 | Catch Me If You Can: How Geo-Indistinguishability Affects Utility In Mobility-Based Geographic Datasets |
| Adriano Di Luzio, Sapienza University of Rome, Italy | ||
| Aline Carneiro Viana, INRIA, France | ||
| Konstantinos Chatzikokolakis, University of Athens, Greece | ||
| Georgi Dikov INRIA, France | ||
| Catuscia Palamidessi LIX, Ecole Polytechnique, France | ||
| Julinda Stefa, Sapienza University of Rome, Italy | ||
| 15:00 | 15:30 | Bokeh: Obfuscating Physical Infrastructure Maps |
| Yugali Gullapalli, University of Wisconsin-Madison, WI, USA | ||
| Jeremy Koritzinsky, University of Wisconsin-Madison, WI, USA | ||
| Meenakshi Syamkumar, University of Wisconsin-Madison, WI, USA | ||
| Paul Barford, University of Wisconsin-Madison, WI, USA | ||
| Ramakrishnan Durairajan, University of Oregon, OR, USA | ||
| Joel Sommers, Colgate University, NY, USA | ||
| 15:30 | 16:00 | Coffee Break |
| Session 4: Geoadvertising and Location-Based Services | ||
| 16:00 | 16:45 | Keynote 2 - Geo-marketing and Situated Consumers: Opportunities and Challenges |
| Syagnik Banerjee, University of Michigan-Flint, MI, USA | ||
| 16:45 | 17:00 | Uplift modeling for location-based online advertising |
| Shogo Kawanaka, Nara Institute of Science and Technology, Japan | ||
| Daisuke Moriwaki, CyberAgent Inc, Japan | ||
| 17:00 | 17:15 | SeMap: Automatic Semantic Mapping for Large-Scale Indoor Spaces |
| Xiaoqiang Teng, DiDi Chuxing, China | ||
| Deke Guo, National University of Defense Technology, China | ||
| Yulan Guo, National University of Defense Technology, China | ||
| Pengfei Xu, DiDi Chuxing, China | ||
| Yiping Meng, DiDi Chuxing, China | ||
| Runbo Hu, DiDi Chuxing, China | ||
| Hua Chai, DiDi Chuxing, China | ||
| Zhong Liu, National University of Defense Technology, China | ||
| 17:15 | 17:30 | A Location-based Social Network System Integrating Mobile Augmented Reality and User Generated Content |
| Yuanwen Yue, Wuhan University, China | ||
| Jiaqi Ding, Wuhan University, China | ||
| Yuhao Kang, University of Wisconsin-Madison, WI, USA | ||
| Yueyao Wang, Peking University, China | ||
| Kunlin Wu, Wuhan University, China | ||
| Teng Fei, Wuhan University, China | ||
| 17:30 | 17:40 | Closing Remarks |
Keynotes
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Keynote 1 - Siva Ravada, Oracle Corporation, USA Spatial Analytics for storing and analyzing GeoSocial data for Geoadvertising In this talk, I will present some fundamental spatial analytics required to analyze and generate derived data products that can be used for GeoAdvertising. There is vast amounts of data available in public domain (OSM) and GeoSocial data with location attributes. This raw data is often unwieldy and location based services for Geoadvertising cannot readily use this data. We first need to process this data and generate data products that can be consumed by traditional software used in these industries. In this talk, I will present some of the challenges in managing this vast amount of data and how spatial analytics can be used to process this raw data and convert it into more manageable data products that can be consumed by traditional LBS applications. |
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Keynote 2 - Syagnik Banerjee, University of Michigan-Flint, MI, USA Geo-marketing and Situated Consumers: Opportunities and Challenges With the increasing abundance of accessible location data, marketers have been targeting consumers with proximity filters to generate campaigns that are more effective. However, the ubiquitous availability of internet-connected devices have not only presented more consumption opportunities, they have also initiated more response variations to marketing surveillance. In this talk, I summarize some of the marketing practices that are executed with location data, insights derived from location data and behavioral transformations in consumers in the context of Geo-surveillance. More specifically, I discuss the role of physical, social and temporal contexts on promotion effectiveness, utility of location tools in determining advertising transparency, consumer segmentation and the personalization-driven elicitation of location privacy concerns. |
