Seattle, WA, USA

Workshop Program

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All times shown are in UTC-7, which is the pacific daylight timezone (PDT).
The workshop starts at 09:00 UTC-7, i.e., 12:00 EDT (New York), 17:00 CET (Brussels), 00:00 CST next day (Beijing).

Session 1
09:00  09:10   Opening Remarks
09:10 10:00 Keynote - Utilizing Location-based Social Media for Trip Mining and Recommendation
Wolfgang Wörndl, TU Munich, Germany
10:00 10:30 Coffee Break
Session 2
10:30 11:00 DRIFT: E2EE spatial feature sharing & instant messaging
Mikael Brunila, McGill University, Canada
Michael McConnell, University of Vermont, VE, USA
Stalgia Grigg, Drift Map, NY, USA
Michael Appuhn, Drift Map, NY, USA
Bethany Sumner, Drift Map, NY, USA
Mitchell Bohman, Drift Map, NY, USA
11:00 11:30 Co-location and Air Pollution Exposure: Case Studies on the Usefulness of Location Privacy
Liyue Fan, University of North Carolina, Charlotte, NC, USA
Julius Marinak, University of North Carolina, Charlotte, NC, USA
Ashley Bang, University of North Carolina, Charlotte, NC, USA
11:30 11:50 Fuzzy Representation of Vague Spatial Descriptions in Real Estate Advertisements
Lucie Cadorel, Université Côte d’Azur, Inria, CNRS, I3S, KCityLabs, France
Denis Overal, KCityLabs, France
Andrea G. B. Tettamanzi, Université Côte d’Azur, Inria, CNRS, I3S, France
11:50 14:00 Lunch Break
Session 3
14:00 14:30 Preference Aware Route Recommendation Using One Billion Geotagged Tweets
Osei Yamashita, Tokyo Metropolitan University, Japan
Shohei Yokoyama, Tokyo Metropolitan University, Japan
14:30 14:50 Addressing the Location A/B problem on Twitter: The next generation location inference research
Rabindra Lamsal, University of Melbourne, Australia
Aaron Harwood, University of Melbourne, Australia
Maria Rodriguez Read, University of Melbourne, Australia
14:50 15:10 Doing groceries again: Towards a recommender system for grocery stores selection
Daniyal Kazempour, Christian-Albrechts-University Kiel, Germany
Melanie Oelker, Christian-Albrechts-University Kiel, Germany
Peer Kröger, Christian-Albrechts-University Kiel, Germany
15:10  15:20  Closing Remarks


Keynote - Wolfgang Wörndl, TU Munich, Germany

Utilizing Location-based Social Media for Trip Mining and Recommendation

Delivering personalized and timely information is particularly valuable in mobile scenarios such as traveling users. Therefore it is desirable to assist the user by services that are tailored towards her context, e.g. the location. Recommender systems recommend movies, restaurants or other items to an active user based on information about users and items, and can be an effective method to support travelers. However, this domain poses special challenges such as high risk, data sparseness, cold start and the complexity of combining items [1]. This talk will first present a data-driven method to mine trips from location-based social networks (LBSN) to better understand traveler mobility [2]. These trips can be quantified using a number of metrics to capture the underlying travel patterns. We outline two use cases to utilize the mined trips: clustering travelers and also destinations.
In the second part, we describe an approach to generate routes consisting of sequences of points-of-interests (POIs) for city walking tours [3]. We discover and score POIs based on retrieving places from the LSBN Foursquare, so it is not restricted to certain cities or regions. Discovered places are then combined to a practical route using Dijkstra’s shortest path algorithm. We show how to find trips that maximizing the value for a user while respecting time and budget constraints. The solution has been implemented in a practical web and mobile application. Since people often travel in groups, we have also looked into different strategies that can be used to recommend POI sequences to groups [4]. We propose a novel strategy called Split Group, which allows groups to split into smaller groups during a trip. The methodology can be generalized to recommend other complex composite items. We conclude the talk with a summary and an outline of current and future challenges for tourism recommender systems which includes multistakeholder aspects and fairness issues.

[1] Herzog, D., Dietz, L. W., & Wörndl, W. (2019). Tourist trip recommendations—foundations, state of the art and challenges. Personalized Human-Computer Interaction, 6, 159-182.
[2] Dietz, L. W., Sen, A., Roy, R., & Wörndl, W. (2020). Mining trips from location-based social networks for clustering travelers and destinations. Information Technology & Tourism, 22(1), 131-166.
[3] Wörndl, W., Hefele, A., & Herzog, D. (2017). Recommending a sequence of interesting places for tourist trips. Information Technology & Tourism, 17(1), 31-54.
[4] Herzog, D., & Wörndl, W. (2019, September). User-centered evaluation of strategies for recommending sequences of points of interest to groups. In Proceedings of the 13th ACM Conference on Recommender Systems (pp. 96-100).


Wolfgang Wörndl is a senior researcher and lecturer at the School of Computation, Information and Technology (CIT) at Technische Universität München (TUM). His research focuses on human-centered and interactive recommender systems in mobile scenarios such as travel and tourism. He has published over 100 refereed papers in related areas. He was co-organizing and served as program committee member for a large number of journals, conferences and workshops, including the ACM RecSys workshop series on Recommenders in Tourism.

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