POI Recommendation with LLM-Augmented Multi-Graph Learning and Contrastive Alignment
Point-of-interest (POI) recommendation models based on graph neural networks achieve strong performance by propagating collaborative signals over user-item interactions, yet they struggle with the cold-start problem, where items with few or no interactions are not represented. In this paper, we propose LLM-augmented Multi-Graph Contrastive Learning (LLM-MGCL), a multi-graph neural network that uses semantic and spatial information about items to extend the LightGCN backbone with two auxiliary item-item graphs: a semantic graph constructed from sentence embeddings of LLM-generated photo summaries and keywords, and a geographic graph derived from Haversine distances between business locatio...
| Authors | Burak Tamer, Wolfram Höpken, Zehui Wang |
|---|---|
| Primary category | cs.IR |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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