Point of Interest Recommendation: Pitfalls and Viable Solutions

2025-07-18

AI Summary

POI recommendation faces three critical real-world bottlenecks: the absence of standardized benchmark datasets, overreliance on unrealistic modeling assumptions (e.g., i.i.d. user behavior), and evaluation frameworks that neglect multi-stakeholder requirements and contextual biases. Through a critical literature review, this work systematically identifies structural deficiencies across data, algorithmic, and evaluation dimensions—integrating behavioral bias modeling, context-aware mechanisms, and empirically grounded assessment protocols. It proposes a practical research agenda centered on multi-stakeholder co-design, trustworthy recommendation mechanisms, novel human–place interaction paradigms, and real-world evaluation infrastructure. The paper culminates in a structured roadmap spanning problem diagnosis, methodological reconstruction, and validation pathways—enhancing both scientific rigor and deployability of POI recommendation systems. (132 words)

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Search and Optimization: Evaluation and Analysis
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

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.

Problem

• Addressing unresolved issues in POI recommendation research
• Identifying shortcomings in datasets, algorithms, and evaluations
• Proposing solutions for multistakeholder design and context awareness

Innovation

• Standardized benchmark datasets for POI recommendation
• Context-aware and multistakeholder algorithm design
• Bias-aware evaluation methodologies for real-world applicability