# NEXT: A Neural Network Framework for Next POI Recommendation

arXiv: https://arxiv.org/abs/1704.04576 · HTML: https://mlsanity.com/paper/1704.04576/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2017.

## Built on by (most cited first, 7 of 7)

- [STAN: Spatio-Temporal Attention Network for Next Location Recommendation](https://mlsanity.com/paper/2102.04095/) (2021)
- [LightMove: A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising](https://mlsanity.com/paper/2108.04993/) (2021)
- [Modelling of Bi-directional Spatio-Temporal Dependence and Users' Dynamic Preferences for Missing POI Check-in Identification](https://mlsanity.com/paper/2112.15285/) (2021)
- [SanMove: Next Location Recommendation via Self-Attention Network](https://mlsanity.com/paper/2112.09076/) (2021)
- [PG2Net: Personalized and Group Preferences Guided Network for Next Place Prediction](https://mlsanity.com/paper/2110.08266/) (2021)
- [Predicting Next Useful Location With Context-Awareness: The State-Of-The-Art](https://mlsanity.com/paper/2401.08081/) (2024)
- [Exploiting Bi-directional Global Transition Patterns and Personal Preferences for Missing POI Category Identification](https://mlsanity.com/paper/2201.00014/) (2021)

## Abstract (arXiv)

The task of next POI recommendation has been studied extensively in recent years. However, developing an unified recommendation framework to incorporate multiple factors associated with both POIs and users remains challenging, because of the heterogeneity nature of these information. Further, effective mechanisms to handle cold-start and endow the system with interpretability are also difficult topics. Inspired by the recent success of neural networks in many areas, in this paper, we present a simple but effective neural network framework for next POI recommendation, named NEXT. NEXT is an unified framework to learn the hidden intent regarding user's next move, by incorporating different factors in an unified manner. Specifically, in NEXT, we incorporate meta-data information and two kinds of temporal contexts (i.e., time interval and visit time). To leverage sequential relations and geographical influence, we propose to adopt DeepWalk, a network representation learning technique, to encode such knowledge. We evaluate the effectiveness of NEXT against state-of-the-art alternatives and neural networks based solutions. Experimental results over three publicly available datasets demonstrate that NEXT significantly outperforms baselines in real-time next POI recommendation. Further experiments demonstrate the superiority of NEXT in handling cold-start. More importantly, we show that NEXT provides meaningful explanation of the dimensions in hidden intent space.