# Category-Aware Location Embedding for Point-of-Interest Recommendation

## TL;DR

This paper introduces a novel neural embedding model for POI recommendation that integrates sequential check-in data and categorical information, significantly improving recommendation accuracy.

## Contribution

The paper proposes a new neural model combining check-in sequences and POI categories, addressing the lack of categorical information in previous models.

## Key findings

- Model outperforms state-of-the-art methods on large-scale datasets
- Incorporating category information improves recommendation quality
- Sequential check-in data enhances geographical relevance understanding

## Abstract

Recently, Point of interest (POI) recommendation has gained ever-increasing importance in various Location-Based Social Networks (LBSNs). With the recent advances of neural models, much work has sought to leverage neural networks to learn neural embeddings in a pre-training phase that achieve an improved representation of POIs and consequently a better recommendation. However, previous studies fail to capture crucial information about POIs such as categorical information. In this paper, we propose a novel neural model that generates a POI embedding incorporating sequential and categorical information from POIs. Our model consists of a check-in module and a category module. The check-in module captures the geographical influence of POIs derived from the sequence of users' check-ins, while the category module captures the characteristics of POIs derived from the category information. To validate the efficacy of the model, we experimented with two large-scale LBSN datasets. Our experimental results demonstrate that our approach significantly outperforms state-of-the-art POI recommendation methods.