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

• URL: http://arxiv.org/abs/2308.07426v4
• Date: Thu, 05 Dec 2024 11:04:14 GMT
• Title: A Survey on Point-of-Interest Recommendations Leveraging Heterogeneous Data
• Authors: Zehui Wang, Wolfram Höpken, Dietmar Jannach,
• Abstract summary: Tourism is an important application domain for recommender systems. Providing POI recommendations to tourists can however be especially challenging due to the variability of the user's context. We provide a survey of published research on the problem of POI recommendation between 2021 and 2023.
• Score: 6.668386678519076
• License:
• 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.

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