# Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation

arXiv: https://arxiv.org/abs/2106.15814 · HTML: https://mlsanity.com/paper/2106.15814/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2021.

## Built on by (most cited first, 20 of 20)

- [GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation](https://mlsanity.com/paper/2303.04741/) (2023)
- [A Diffusion model for POI recommendation](https://mlsanity.com/paper/2304.07041/) (2023)
- [Lightweight Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/2108.11333/) (2021)
- [Decentralized Collaborative Learning Framework for Next POI Recommendation](https://mlsanity.com/paper/2204.06516/) (2022)
- [Revisiting Mobility Modeling with Graph: A Graph Transformer Model for Next Point-of-Interest Recommendation](https://mlsanity.com/paper/2310.01224/) (2023)
- [Graph Neural Network for spatiotemporal data: methods and applications](https://mlsanity.com/paper/2306.00012/) (2023)
- [Predicting Human Mobility via Self-supervised Disentanglement Learning](https://mlsanity.com/paper/2211.09625/) (2022)
- [Hyper-Relational Knowledge Graph Neural Network for Next POI](https://mlsanity.com/paper/2311.16683/) (2023)
- [REPLAY: Modeling Time-Varying Temporal Regularities of Human Mobility for Location Prediction over Sparse Trajectories](https://mlsanity.com/paper/2402.16310/) (2024)
- [Beyond Regularity: Modeling Chaotic Mobility Patterns for Next Location Prediction](https://mlsanity.com/paper/2509.11713/) (2025)
- [Self-supervised Graph-based Point-of-interest Recommendation](https://mlsanity.com/paper/2210.12506/) (2022)
- [DARLR: Dual-Agent Offline Reinforcement Learning for Recommender Systems with Dynamic Reward](https://mlsanity.com/paper/2505.07257/) (2025)
- [Bi-Level Graph Structure Learning for Next POI Recommendation](https://mlsanity.com/paper/2411.01169/) (2024)
- [Next Point-of-interest (POI) Recommendation Model Based on Multi-modal Spatio-temporal Context Feature Embedding](https://mlsanity.com/paper/2509.22661/) (2025)
- [Joint Triplet Loss Learning for Next New POI Recommendation](https://mlsanity.com/paper/2209.12162/) (2022)
- [A Dataset for Spatiotemporal-Sensitive POI Question Answering](https://mlsanity.com/paper/2505.10928/) (2025)
- [STRelay: A Universal Spatio-Temporal Relaying Framework for Location Prediction over Human Trajectory Data](https://mlsanity.com/paper/2508.16620/) (2025)
- [Exploring Urban Factors with Autoencoders: Relationship Between Static and Dynamic Features](https://mlsanity.com/paper/2509.06167/) (2025)
- [Empowering Denoising Sequential Recommendation with Large Language Model Embeddings](https://mlsanity.com/paper/2510.04239/) (2025)
- [Think Before You Act: Intention-Guided Reasoning for LLM-Based Location Prediction](https://mlsanity.com/paper/2606.08122/) (2026)

## Abstract (arXiv)

Being an indispensable component in location-based social networks, next point-of-interest (POI) recommendation recommends users unexplored POIs based on their recent visiting histories. However, existing work mainly models check-in data as isolated POI sequences, neglecting the crucial collaborative signals from cross-sequence check-in information. Furthermore, the sparse POI-POI transitions restrict the ability of a model to learn effective sequential patterns for recommendation. In this paper, we propose Sequence-to-Graph (Seq2Graph) augmentation for each POI sequence, allowing collaborative signals to be propagated from correlated POIs belonging to other sequences. We then devise a novel Sequence-to-Graph POI Recommender (SGRec), which jointly learns POI embeddings and infers a user's temporal preferences from the graph-augmented POI sequence. To overcome the sparsity of POI-level interactions, we further infuse category-awareness into SGRec with a multi-task learning scheme that captures the denser category-wise transitions. As such, SGRec makes full use of the collaborative signals for learning expressive POI representations, and also comprehensively uncovers multi-level sequential patterns for user preference modelling. Extensive experiments on two real-world datasets demonstrate the superiority of SGRec against state-of-the-art methods in next POI recommendation.