# NEXT: A Neural Network Framework for Next POI Recommendation

## TL;DR

NEXT is a neural network framework that effectively integrates multiple factors, including metadata, temporal contexts, and geographical influence, to improve next POI recommendation, especially in cold-start scenarios, while providing interpretability.

## Contribution

The paper introduces NEXT, a unified neural network framework that incorporates diverse data sources and contexts for improved next POI recommendation and interpretability.

## Key findings

- Outperforms state-of-the-art methods on three datasets
- Handles cold-start scenarios effectively
- Provides meaningful explanations of user intent dimensions

## Abstract

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.