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A Survey on Point-of-Interest Recommendations Leveraging Heterogeneous Data

Zehui Wang, Wolfram Höpken, Dietmar Jannach

TL;DR

This survey addresses the challenge of POI recommendations in tourism where heterogeneous data sources offer potential for more accurate, context-aware personalization. It systematically reviews 2021–2023 literature to map information types, integration approaches, and evaluation methodologies, revealing a heavy reliance on check-in data and a gap in exploiting richer information modalities. The study highlights a shift toward embedding-based and graph-based techniques, and identifies reproducibility and diverse evaluation metrics as key future concerns. By framing data types and integration strategies, the paper provides a roadmap for developing more information-rich and transparent POI recommender systems with broader real-world impact.

Abstract

Tourism is an important application domain for recommender systems. In this domain, recommender systems are for example tasked with providing personalized recommendations for transportation, accommodation, points-of-interest (POIs), etc. Among these tasks, in particular the problem of recommending POIs that are of likely interest to individual tourists has gained growing attention in recent years. Providing POI recommendations to tourists can however be especially challenging due to the variability of the user's context. With the rapid development of the Web and today's multitude of online services, vast amounts of data from various sources have become available, and these heterogeneous data represent a huge potential to better address the challenges of POI recommendation problems. In this work, we provide a survey of published research on the problem of POI recommendation between 2021 and 2023. The literature was surveyed to identify the information types, techniques and evaluation methods employed. Based on the analysis, it was observed that the current research tends to focus on a relatively narrow range of information types and there is a significant potential in improving POI recommendation by leveraging heterogeneous data. As the first information-centric survey on POI recommendation research, this study serves as a reference for researchers aiming to develop increasingly accurate, personalized and context-aware POI recommender systems.

A Survey on Point-of-Interest Recommendations Leveraging Heterogeneous Data

TL;DR

This survey addresses the challenge of POI recommendations in tourism where heterogeneous data sources offer potential for more accurate, context-aware personalization. It systematically reviews 2021–2023 literature to map information types, integration approaches, and evaluation methodologies, revealing a heavy reliance on check-in data and a gap in exploiting richer information modalities. The study highlights a shift toward embedding-based and graph-based techniques, and identifies reproducibility and diverse evaluation metrics as key future concerns. By framing data types and integration strategies, the paper provides a roadmap for developing more information-rich and transparent POI recommender systems with broader real-world impact.

Abstract

Tourism is an important application domain for recommender systems. In this domain, recommender systems are for example tasked with providing personalized recommendations for transportation, accommodation, points-of-interest (POIs), etc. Among these tasks, in particular the problem of recommending POIs that are of likely interest to individual tourists has gained growing attention in recent years. Providing POI recommendations to tourists can however be especially challenging due to the variability of the user's context. With the rapid development of the Web and today's multitude of online services, vast amounts of data from various sources have become available, and these heterogeneous data represent a huge potential to better address the challenges of POI recommendation problems. In this work, we provide a survey of published research on the problem of POI recommendation between 2021 and 2023. The literature was surveyed to identify the information types, techniques and evaluation methods employed. Based on the analysis, it was observed that the current research tends to focus on a relatively narrow range of information types and there is a significant potential in improving POI recommendation by leveraging heterogeneous data. As the first information-centric survey on POI recommendation research, this study serves as a reference for researchers aiming to develop increasingly accurate, personalized and context-aware POI recommender systems.
Paper Structure (41 sections, 14 figures, 4 tables)

This paper contains 41 sections, 14 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background and Preliminaries
  3. Recommender Systems and Their Applications in Tourism
  4. Types of Recommender Systems
  5. CF methods
  6. CB methods
  7. Hybrid methods
  8. Applications of Recommender Systems in Tourism
  9. Problem Formulation
  10. Previous Reviews on Point-of-Interest Recommendation
  11. Research Methodology
  12. Definition of Research Questions
  13. Search Strategy
  14. Screening of Papers
  15. Paper Mapping
  16. ...and 26 more sections

Figures (14)

  • Figure 1: Temporal Phases of the Tourism Process
  • Figure 2: Main Recommendation Tasks in Tourism
  • Figure 3: Mapping Process for Identifying Relevant Papers on POI Recommendations
  • Figure 4: Annual Distribution and Application Focus on POI Recommendations
  • Figure 5: Overview of Data Collection Approaches in POI Recommendation Research
  • ...and 9 more figures