Point of Interest Recommendation: Pitfalls and Viable Solutions

Abstract

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.

References in corpus (14)

• YFCC100M: The New Data in Multimedia Research
• A Survey on Conversational Recommender Systems
• A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research
• Point-of-Interest Recommender Systems based on Location-Based Social Networks: A Survey from an Experimental Perspective
• A Critical Study on Data Leakage in Recommender System Offline Evaluation
• COVID-19 is linked to changes in the time-space dimension of human mobility
• Towards Individual and Multistakeholder Fairness in Tourism Recommender Systems
• A Survey on Point-of-Interest Recommendations Leveraging Heterogeneous Data
• Health-promoting Potential of Parks in 35 Cities Worldwide
• Biases in human mobility data impact epidemic modeling
• Multi-User Chat Assistant (MUCA): a Framework Using LLMs to Facilitate Group Conversations
• Predicting Group Choices from Group Profiles
• SynthTRIPs: A Knowledge-Grounded Framework for Benchmark Query Generation for Personalized Tourism Recommenders
• Ethical and Privacy Considerations with Location Based Data Research