Research Projects

Urban Parks for Promoting Health

In collaboration with Sanja Šćepanović, Ke Zhou, Daniele Quercia (Nokia Bell Labs, UK), and André Felipe Zanella ( Telefónica, Spain)

Urban parks are essential for promoting health through recreation and leisure, but their specific roles in supporting diverse health-enhancing activities have been underexplored. Traditional studies often focus on size, greenery, or accessibility, overlooking how individual park spaces and elements contribute to health. To address this, we developed a taxonomy of six health-promoting activity categories—physical, mind-body, nature appreciation, environmental, social, and cultural—and assessed parks in 35 global cities using data on 23,477 parks and 827,038 features from OpenStreetMap.

Our analysis revealed distinct trends: North American parks prioritize physical activities, while European parks favor nature appreciation. Parks in city centers typically offered more diverse activities compared to suburban ones, and significant disparities in park standards were evident across cities. While Tokyo and Paris showed uniform park quality, cities like Copenhagen and Rio de Janeiro had more pronounced inequalities. These findings underscore the need for equitable, health-promoting urban park designs.

In cities with extensive park systems, like London, we explored how recommender systems could help residents discover parks that match their leisure and health preferences. Using data from a resident survey and over a million geotagged Flickr images, we found that proximity-based recommendations often fell short, favoring popular parks over lesser-known ones. Personalized models effectively addressed these biases, broadening the range of parks highlighted and better aligning with user preferences. This research highlights the potential of recommendation systems to connect urban populations with parks that promote well-being.

Publications

• Examining Inequality in Park Quality for Promoting Health Across 35 Global Cities. Preprint [Visualisation]
• Exploratory Analysis of Recommending Urban Parks for Health-Promoting Activities. ACM RecSys’24

Analysis of Point-of-Interest Recommendation

In collaboration with Pablo Sánchez (Universidad Pontificia Comillas, Spain), Alejandro Bellogín (Universidad Autónoma de Madrid, Spain)

• Understanding the Influence of Data Characteristics on the Performance of Point-of-Interest Recommendation Algorithms. Preprint
• Exploratory Analysis of Recommending Urban Parks for Health-Promoting Activities. ACM RecSys’24
• Travelers vs. Locals: The Effect of Cluster Analysis in Point-of-Interest Recommendation. ACM UMAP’22

Data-Driven Destination Recommender Systems

My doctoral research project at the Technical University of Munich under the supervision of Wolfgang Wörndl and Jörg Ott.

Choosing where to travel is a complex and emotionally significant decision, yet systems to guide destination selection remain underdeveloped compared to those for hotels or restaurants. In our doctoral research, we tackled this gap by focusing on traveler mobility analysis, destination characterization, and conversational recommender systems, aiming to make travel planning more personalized and effective.

To better understand traveler behavior, we analyzed global mobility patterns using data from location-based social networks. Cluster analyses revealed distinct traveler types, showing that ignoring these groups can lead to inaccurate recommendations. We also developed a hierarchical map of travel regions and methods to recommend personalized stay durations, enhancing the relevance of suggestions.

For destination characterization, we evaluated 18 data models, finding that textual sources like Wikipedia effectively captured the essence of “touristic experiences.” By optimizing feature-based models with black-box techniques, we made them competitive with implicit approaches, ensuring they better matched traveler preferences.

Recognizing the challenges users face in articulating preferences and managing expectations, we designed CityRec, a conversational recommender system. Our “Navigation by Revealing Trade-offs” approach helped users clarify their priorities and compromises, making decision-making more informed and satisfying. A study of 600 participants confirmed the system’s effectiveness, demonstrating the value of integrating intuitive design with robust algorithms.

This research advanced destination recommendation by offering new insights into traveler mobility, refining destination characterization techniques, and introducing a user-centered conversational framework to improve travel planning experiences.

Publications

• Data-Driven Destination Recommender Systems. TUM’23
• Recommending the Duration of Stay in Personalized Travel Recommender Systems. ACM RecTour’22
• Navigation by Revealing Trade-offs for Content-based Recommendations. ENTER’22
• A Comparative Study of Data-driven Models for Travel Destination Characterization. Frontiers in Big Data’22
• Travelers vs. Locals: The Effect of Cluster Analysis in Point-of-Interest Recommendation. ACM UMAP’22
• Triprec – a Recommender System for Planning Composite City Trips Based on Travel Mobility Analysis. ACM WebTour’21.
• Analyzing ‘Near Me’ Services: Potential for Exposure Bias in Location-based Retrieval. IEEE FILA’20.
• Mining Trips from Location-based Social Networks for Clustering Travelers and Destinations. JITT’20
• CityRec — a Data-driven Conversational Destination Recommender System. e-RTR’20
• Designing a Conversational Travel Recommender System Based on Data-driven Destination Characterization. ACM RecTour’19
• How Long to Stay Where? On the Amount of Item Consumption in Travel Recommendation. ACM RecSys’19
• Tourist Trip Recommendations – Foundations, State of the Art and Challenges. Personalized Human-Computer Interaction
• Modeling Physiological Conditions for Proactive Tourist Recommendations. ACM ABIS’19.
• Identifying Travel Regions Using Location-based Social Network Check-in Data. Frontiers in Big Data’19
• Data-Driven Destination Recommender Systems. In: 26th Conference on User Modeling, Adaptation and Personalization. UMAP ’18. New York, NY, USA: ACM, July 2018, pp. 257–260. doi: 10.1145/3209219.3213591.
• Deriving Tourist Mobility Patterns from Check-in Data. ACM LearnIR’18.
• Characterisation of Traveller Types Using Check-in Data from Location-based Social Networks. ENTER’18
• Recommending Crowdsourced Trips on wOndary. ACM RecTour’18