KGNext
Knowledge-Graph-Enhanced Transformer for Next POI Recommendation With Uncertain Check-Ins
Bibliographic Data
| ID | 22106994 |
|---|---|
| Authors | Xiangjie Kong (0000-0003-2698-3319, Zhejiang University of Technology), Zhiyu Chen (0000-0001-7937-642X, Zhejiang University of Technology), Jianxin Li (0000-0001-9166-642X, Deakin University), Junhui Bi (0009-0004-3521-1366, Zhejiang University of Technology), Guojiang Shen (0000-0003-1064-1250, Zhejiang University of Technology) |
| Year | 2024 |
| Volume | 11 |
| Issue | 5 |
| Pages | 6637-6648 |
| Publication date | 2024-10-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2024.3396506 |
| OpenAlex | W4399168326 |
| Language | EN |
| Citations received | 6 |
| References cited | 38 |
The next point-of-interest (POI) recommendation aims to predict users’ future movements based on their historical trajectories. However, in reality, users may provide uncertain check-in records, resulting in uploaded data that lack precise location information and is instead ambiguous. Despite this challenge, only a limited number of studies have addressed this issue, often overlooking the intricate interactions among users, POIs, and POI categories. To that end, we propose a novel model called knowledge-graph-enhanced transformer (KGNext). KGNext leverages transition and interaction graphs derived from our constructed transitional-interactive knowledge graph (TIKG) to uncover both general movement patterns and varied user preferences regarding POIs and POI categories. Furthermore, KGNext integrates comprehensive contextual information from historical trajectories with TIKG to generate user trajectory embeddings. These encoded features are then utilized by a transformer model to provide fine-grained predictions of the next POI. Experimental results on three real-world datasets demonstrate the superiority of KGNext
Electrical engineering · Graph · Knowledge graph · Transformer · Voltage · Advanced Graph Neural Networks · Computer Science · Engineering · Recommender Systems and Techniques · Topic Modeling · Artificial Intelligence · Theoretical Computer Science
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Predicting the Next Location of Urban Individuals via a Representation-Enhanced Multi-View Learning Network
| Citations per year | 6 |
|---|---|
| Citation span | 2025 - 2026 (2) |
| Highly cited | No |