Knowledge Graph Dataset used in DecKG
Description
1. Research Hypothesis: The proposed method DecKG aims to improve location recommendation systems by integrating users' check-in behavior (user-POI interactions) with structured urban knowledge graphs (KGs). The hypothesis is that leveraging contextual information from KGs—such as POI categories, geographic proximity, and brand affiliations—will enhance the accuracy and relevance of personalized POI recommendations. 2. Data Description: 2.1 Datasets: Two real-world datasets, Beijing and Shanghai [1], are used. Each contains check-in histories of 10,000 users and a corresponding knowledge graph. 2.2 User-POI Interactions: These represent users' historical check-ins at Points of Interest (POIs), capturing preferences and behavior. 2.3 Knowledge Graph (KG): The KG is structured as triplets (head entity, relation, tail entity). Entities include POIs (e.g., a restaurant), their categories (e.g., "Chinese cuisine"), geographic segments (e.g., a district), and attributes like brand. Relations define contextual links between entities, such as "belongs to brand X" or "is near [region]." 2.4 Data Split: Each dataset is divided into training (80%), testing (10%), and validation (10%) sets to evaluate model performance. 3. Data Collection: 3.1 User check-in data was sourced from location-based services, reflecting real-world visitation patterns as introduced in [1]. 3.2 The KG was constructed using POI metadata (e.g., categories, brands) and spatial relationships (e.g., proximity between regions) as introduced in [1]. 3.3 The datasets were preprocessed to select 10,000 active users per city, ensuring balanced representation. [1] Liu, C., Gao, C., Jin, D., Li, Y., 2021. Improving location recommendation with urban knowledge graph. arXiv preprint arXiv:2111.01013.