arXiv subjects

Linus W. Dietz

Publications and source records attributed to Linus W. Dietz.

9 recordsLinked to original sources

arXiv2026

Mobile Application Traffic Reveals Multifunctional Use Patterns in Parisian Parks

Urban parks play a key role in supporting public health. Landscape architecture typically considers parks through the lens of form and function. While past research on equitable access has focused mainly on park form, studies addressing functional uses have been constrained by limited scale and coarse measurement techniques. Existing efforts have partially quantified park functions through small-scale surveys and movement data or general usage data, but have not effectively captured the specific activities and motivations underlying park visits. As a result, our understanding of the functional roles urban parks play remains incomplete. We introduce a novel method that refines mobile base station coverage using antenna azimuths, enabling more precise distinction of mobile traffic within parks versus surrounding areas. Using Paris as a case study, we analyze a large-scale dataset of passively collected per-app mobile network traffic across 45 urban parks. We test two hypotheses: the Central-City hypothesis, which posits that multifunctional parks emerge in dense, high-rent areas due to land scarcity; and the Socio-Spatial hypothesis, which views parks as reflections of neighborhood routines and preferences. Our analysis shows that parks have distinctive mobile traffic signatures, differing from both their urban surroundings and from each other and identify three distinct functional types, Lunchbreak, Cultural, and Recreational parks, and analyze the traffic usage toward different motivations for visitation. Centrally located parks display more diverse app usage and pronounced temporal variation, while suburban parks reflect the digital behaviors of nearby communities, with app preferences aligned to neighborhood income. These findings demonstrate the value of mobile traffic as a proxy for studying the diversity of usage and activities within urban green spaces.

cs.CY↗

arXiv2026

Data Product MCP: Chat with your Enterprise Data

Computational data governance aims to make the enforcement of governance policies and legal obligations more efficient and reliable. Recent advances in natural language processing and agentic AI offer ways to improve how organizations share and use data. But many barriers remain. Today's tools require technical skills and multiple roles to discover, request, and query data. Automating data access using enterprise AI agents is limited by the means to discover and autonomously access distributed data. Current solutions either compromise governance or break agentic workflows through manual approvals. To close this gap, we introduce Data Product MCP integrated in a data product marketplace. This data marketplace, already in use at large enterprises, enables AI agents to find, request, and query enterprise data products while enforcing data contracts in real time without lowering governance standards. The system is built on the Model Context Protocol (MCP) and links the AI-driven marketplace with cloud platforms such as Snowflake, Databricks, and Google Cloud Platform. It supports semantic discovery of data products based on business context, automates access control by validating generated queries against approved business purposes using AI-driven checks, and enforces contracts in real time by blocking unauthorized queries before they run. We assessed the system with feedback from n=16 experts in data governance. Our qualitative evaluation demonstrates effectiveness through enterprise scenarios such as customer analytics. The findings suggest that Data Product MCP reduces the technical burden for data analysis without weakening governance, filling a key gap in enterprise AI adoption.

cs.ET↗

arXiv2025

Health-promoting Potential of Parks in 35 Cities Worldwide

Urban parks are important for public health, but the role of specific spaces, such as playgrounds or lakes, and elements, such as benches or sports equipment, in supporting well-being is not well understood. Based on expert input and a review of the literature, we defined six types of health-related activities: physical, mindfulness, nature appreciation, environmental, social, and cultural. We built a lexicon that links each activity to specific elements and spaces within parks present in OpenStreetMap. Using this data, we scored 23,477 parks across 35 cities worldwide based on their ability to support these activities. We found clear patterns: parks in North America focus more on physical activity, while those in Europe offer more chances to enjoy nature. Parks near city centers support health-promoting activities better than those farther out. Suburban parks in many cities lack the spaces and equipment needed for nature-based, social, and cultural activities. We also found large gaps in park quality between cities. Tokyo and Paris provide more equal access, while Copenhagen and Rio de Janeiro show sharp contrasts. These results can help cities create fairer parks that better support public health.

cs.CY↗

arXiv2025

Point of Interest Recommendation: Pitfalls and Viable Solutions

Point of interest (POI) recommendation can play a pivotal role in enriching tourists' experiences by suggesting context-dependent and preference-matching locations and activities, such as restaurants, landmarks, itineraries, and cultural attractions. Unlike some more common recommendation domains (e.g., music and video), POI recommendation is inherently high-stakes: users invest significant time, money, and effort to search, choose, and consume these suggested POIs. Despite the numerous research works in the area, several fundamental issues remain unresolved, hindering the real-world applicability of the proposed approaches. In this paper, we discuss the current status of the POI recommendation problem and the main challenges we have identified. The first contribution of this paper is a critical assessment of the current state of POI recommendation research and the identification of key shortcomings across three main dimensions: datasets, algorithms, and evaluation methodologies. We highlight persistent issues such as the lack of standardized benchmark datasets, flawed assumptions in the problem definition and model design, and inadequate treatment of biases in the user behavior and system performance. The second contribution is a structured research agenda that, starting from the identified issues, introduces important directions for future work related to multistakeholder design, context awareness, data collection, trustworthiness, novel interactions, and real-world evaluation.

cs.IR↗

arXiv2025

The Experience of Running: Recommending Routes Using Sensory Mapping in Urban Environments

Depending on the route, runners may experience frustration, freedom, or fulfilment. However, finding routes that are conducive to the psychological experience of running remains an unresolved task in the literature. In a mixed-method study, we interviewed 7 runners to identify themes contributing to running experience, and quantitatively examined these themes in an online survey with 387 runners. Using Principal Component Analysis on the survey responses, we developed a short experience sampling questionnaire that captures the three most important dimensions of running experience: \emph{performance \& achievement}, \emph{environment}, and \emph{mind \& social connectedness}. Using path preferences obtained from the online survey, we clustered them into two types of routes: \emph{scenic} (associated with nature and greenery) and \emph{urban} (characterized by the presence of people); and developed a routing engine for path recommendations. We discuss challenges faced in developing the routing engine, and provide guidelines to integrate it into mobile and wearable running apps.

cs.HC↗

arXiv2025

Understanding the Influence of Data Characteristics on the Performance of Point-of-Interest Recommendation Algorithms

Point-of-interest (POI) recommendations are essential for travelers and the e-tourism business. They assist in decision-making regarding what venues to visit and where to dine and stay. While it is known that traditional recommendation algorithms' performance depends on data characteristics like sparsity, popularity bias, and preference distributions, the impact of these data characteristics has not been systematically studied in the POI recommendation domain. To fill this gap, we extend a previously proposed explanatory framework by introducing new explanatory variables specifically relevant to POI recommendation. At its core, the framework relies on having subsamples with different data characteristics to compute a regression model, which reveals the dependencies between data characteristics and performance metrics of recommendation models. To obtain these subsamples, we subdivide a POI recommendation data set on New York City and measure the effect of these characteristics on different classical POI recommendation algorithms in terms of accuracy, novelty, and item exposure. Our findings confirm the crucial role of key data features like density, popularity bias, and the distribution of check-ins in POI recommendation. Additionally, we identify the significance of novel factors, such as user mobility and the duration of user activity. In summary, our work presents a generic method to quantify the influence of data characteristics on recommendation performance. The results not only show why certain POI recommendation algorithms excel in specific recommendation problems derived from a LBSN check-in data set in New York City, but also offer practical insights into which data characteristics need to be addressed to achieve better recommendation performance.

cs.IR↗

arXiv2020

Analyzing 'Near Me' Services: Potential for Exposure Bias in Location-based Retrieval

The proliferation of smartphones has led to the increased popularity of location-based search and recommendation systems. Online platforms like Google and Yelp allow location-based search in the form of nearby feature to query for hotels or restaurants in the vicinity. Moreover, hotel booking platforms like Booking[dot]com, Expedia, or Trivago allow travelers searching for accommodations using either their desired location as a search query or near a particular landmark. Since the popularity of different locations in a city varies, certain locations may get more queries than other locations. Thus, the exposure received by different establishments at these locations may be very different from their intrinsic quality as captured in their ratings. Today, many small businesses (shops, hotels, or restaurants) rely on such online platforms for attracting customers. Thus, receiving less exposure than that is expected can be unfavorable for businesses. It could have a negative impact on their revenue and potentially lead to economic starvation or even shutdown. By gathering and analyzing data from three popular platforms, we observe that many top-rated hotels and restaurants get less exposure vis-a-vis their quality, which could be detrimental for them. Following a meritocratic notion, we define and quantify such exposure disparity due to location-based searches on these platforms. We attribute this exposure disparity mainly to two kinds of biases -- Popularity Bias and Position Bias. Our experimental evaluation on multiple datasets reveals that although the platforms are doing well in delivering distance-based results, exposure disparity exists for individual businesses and needs to be reduced for business sustainability.

cs.IR↗

arXiv2019

Online Evaluations for Everyone: Mr. DLib's Living Lab for Scholarly Recommendations

We introduce the first 'living lab' for scholarly recommender systems. This lab allows recommender-system researchers to conduct online evaluations of their novel algorithms for scholarly recommendations, i.e., recommendations for research papers, citations, conferences, research grants, etc. Recommendations are delivered through the living lab's API to platforms such as reference management software and digital libraries. The living lab is built on top of the recommender-system as-a-service Mr. DLib. Current partners are the reference management software JabRef and the CORE research team. We present the architecture of Mr. DLib's living lab as well as usage statistics on the first sixteen months of operating it. During this time, 1,826,643 recommendations were delivered with an average click-through rate of 0.21%.

cs.IR↗

arXiv2019

A Model for Using Physiological Conditions for Proactive Tourist Recommendations

Mobile proactive tourist recommender systems can support tourists by recommending the best choice depending on different contexts related to herself and the environment. In this paper, we propose to utilize wearable sensors to gather health information about a tourist and use them for recommending tourist activities. We discuss a range of wearable devices, sensors to infer physiological conditions of the users, and exemplify the feasibility using a popular self-quantification mobile app. Our main contribution then comprises a data model to derive relations between the parameters measured by the wearable sensors, such as heart rate, body temperature, blood pressure, and use them to infer the physiological condition of a user. This model can then be used to derive classes of tourist activities that determine which items should be recommended.

cs.HC↗