# DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation

arXiv: https://arxiv.org/abs/2210.16591 · HTML: https://mlsanity.com/paper/2210.16591/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 45 references that are in the map.
- First posted on arXiv 29 Oct 2022.

## Builds on (most cited first, 19 of 19)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Representation Learning on Graphs with Jumping Knowledge Networks](https://mlsanity.com/paper/1806.03536/) (2018)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018)
- [Disentangled Graph Collaborative Filtering](https://mlsanity.com/paper/2007.01764/) (2020)
- [Learning Disentangled Representations for Recommendation](https://mlsanity.com/paper/1910.14238/) (2019)
- [Learning Intents behind Interactions with Knowledge Graph for Recommendation](https://mlsanity.com/paper/2102.07057/) (2021)
- [DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation](https://mlsanity.com/paper/2106.10879/) (2021)
- [Disentangling Long and Short-Term Interests for Recommendation](https://mlsanity.com/paper/2202.13090/) (2022)
- [Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective](https://mlsanity.com/paper/2101.02634/) (2021)
- [KGNN: Harnessing Kernel-based Networks for Semi-supervised Graph Classification](https://mlsanity.com/paper/2205.10550/) (2022)
- [Kernel-based Substructure Exploration for Next POI Recommendation](https://mlsanity.com/paper/2210.03969/) (2022)
- [Dynamic Sequential Graph Learning for Click-Through Rate Prediction](https://mlsanity.com/paper/2109.12541/) (2021)

## Built on by (most cited first, 9 of 9)

- [A Diffusion model for POI recommendation](https://mlsanity.com/paper/2304.07041/) (2023)
- [RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification](https://mlsanity.com/paper/2308.02335/) (2023)
- [DisenSemi: Semi-supervised Graph Classification via Disentangled Representation Learning](https://mlsanity.com/paper/2407.14081/) (2024)
- [Next Point-of-interest (POI) Recommendation Model Based on Multi-modal Spatio-temporal Context Feature Embedding](https://mlsanity.com/paper/2509.22661/) (2025)
- [TrajGEOS: Trajectory Graph Enhanced Orientation-based Sequential Network for Mobility Prediction](https://mlsanity.com/paper/2412.19092/) (2024)
- [Where to Go Next Day: Multi-scale Spatial-Temporal Decoupled Model for Mid-term Human Mobility Prediction](https://mlsanity.com/paper/2501.06561/) (2025)
- [Enhancing POI Recommendation through Global Graph Disentanglement with POI Weighted Module](https://mlsanity.com/paper/2507.14612/) (2025)
- [Disentangling Multiplex Spatial-Temporal Transition Graph Representation Learning for Socially Enhanced POI Recommendation](https://mlsanity.com/paper/2508.07649/) (2025)
- [Mag-Mamba: Modeling Coupled spatiotemporal Asymmetry for POI Recommendation](https://mlsanity.com/paper/2603.00053/) (2026)

## Abstract (arXiv)

Point-of-Interest (POI) recommendation plays a vital role in various location-aware services. It has been observed that POI recommendation is driven by both sequential and geographical influences. However, since there is no annotated label of the dominant influence during recommendation, existing methods tend to entangle these two influences, which may lead to sub-optimal recommendation performance and poor interpretability. In this paper, we address the above challenge by proposing DisenPOI, a novel Disentangled dual-graph framework for POI recommendation, which jointly utilizes sequential and geographical relationships on two separate graphs and disentangles the two influences with self-supervision. The key novelty of our model compared with existing approaches is to extract disentangled representations of both sequential and geographical influences with contrastive learning. To be specific, we construct a geographical graph and a sequential graph based on the check-in sequence of a user. We tailor their propagation schemes to become sequence-/geo-aware to better capture the corresponding influences. Preference proxies are extracted from check-in sequence as pseudo labels for the two influences, which supervise the disentanglement via a contrastive loss. Extensive experiments on three datasets demonstrate the superiority of the proposed model.