[Paper Review] User modeling for point-of-interest recommendations in location-based social networks: the state-of-the-art

Shudong Liu|arXiv (Cornell University)|Dec 19, 2017

Human Mobility and Location-Based Analysis3 citations

TL;DR

This paper provides a comprehensive survey of user modeling techniques for point-of-interest (POI) recommendations in location-based social networks (LBSNs), categorizing approaches based on data types: check-in data, geographical information, spatio-temporal patterns, and geo-social features. It formalizes user modeling frameworks and identifies key challenges and future research directions in LBSN recommendation systems.

ABSTRACT

The rapid growth of location-based services(LBSs)has greatly enriched people's urban lives and attracted millions of users in recent years. Location-based social networks(LBSNs)allow users to check-in at a physical location and share daily tips on points-of-interest (POIs) with their friends anytime and anywhere. Such check-in behavior can make daily real-life experiences spread quickly through the Internet. Moreover, such check-in data in LBSNs can be fully exploited to understand the basic laws of human daily movement and mobility. This paper focuses on reviewing the taxonomy of user modeling for POI recommendations through the data analysis of LBSNs. First, we briefly introduce the structure and data characteristics of LBSNs,then we present a formalization of user modeling for POI recommendations in LBSNs. Depending on which type of LBSNs data was fully utilized in user modeling approaches for POI recommendations, we divide user modeling algorithms into four categories: pure check-in data-based user modeling, geographical information-based user modeling, spatio-temporal information-based user modeling, and geo-social information-based user modeling. Finally,summarizing the existing works, we point out the future challenges and new directions in five possible aspects

Motivation & Objective

• To systematically review and categorize user modeling techniques for POI recommendations in LBSNs based on data utilization.
• To formalize the user modeling framework for POI recommendation in LBSNs using structured data analysis.
• To identify limitations and open challenges in current approaches to guide future research.
• To provide a taxonomy of user modeling methods based on the type of LBSN data employed (check-in, geographical, spatio-temporal, geo-social).

Proposed method

• Classifies user modeling approaches into four categories based on data types: pure check-in data, geographical information, spatio-temporal patterns, and geo-social information.
• Analyzes the structure and data characteristics of LBSNs to establish a foundation for user modeling.
• Proposes a formalization of user modeling for POI recommendations using data-driven modeling principles.
• Reviews existing works within each category to identify patterns, strengths, and limitations.
• Synthesizes insights from diverse LBSN data sources to support multi-faceted user modeling.
• Identifies future research directions based on gaps in current methodologies and data utilization.

Experimental results

Research questions

• RQ1 How can user modeling for POI recommendations be systematically categorized based on the data types used?
• RQ2 What are the key characteristics and limitations of user modeling approaches relying solely on check-in data?
• RQ3 How do geographical and spatio-temporal data enhance the accuracy and personalization of POI recommendations?
• RQ4 In what ways does integrating social relationships and user interactions improve recommendation performance?
• RQ5 What are the major open challenges and emerging research directions in LBSN-based POI recommendation systems?

Key findings

• User modeling approaches in LBSNs are best categorized into four distinct types based on data utilization: check-in data, geographical, spatio-temporal, and geo-social information.
• Pure check-in data-based models are widely used but often lack contextual awareness of user mobility patterns.
• Incorporating geographical information improves spatial relevance but may overlook temporal dynamics of user behavior.
• Spatio-temporal models that integrate time and location show higher accuracy in predicting user preferences.
• Geo-social models that combine social ties and check-in behavior yield more personalized recommendations but face scalability and privacy challenges.
• The paper identifies five key future research directions, including handling data sparsity, improving explainability, and leveraging deep learning for complex mobility patterns.