A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations

TL;DR

This paper provides the first comprehensive survey of deep learning-based Point-of-Interest recommendation systems, analyzing various techniques, datasets, and features used in recent research to guide future work.

Contribution

It categorizes and critically analyzes recent deep learning approaches for POI recommendation, serving as a detailed reference for researchers and practitioners.

Findings

Deep learning techniques vary widely in POI recommendation.

Most works utilize multi-modal data including user check-ins and POI attributes.

The survey highlights gaps and future directions in the field.

Abstract

Location-based Social Networks (LBSNs) enable users to socialize with friends and acquaintances by sharing their check-ins, opinions, photos, and reviews. Huge volume of data generated from LBSNs opens up a new avenue of research that gives birth to a new sub-field of recommendation systems, known as Point-of-Interest (POI) recommendation. A POI recommendation technique essentially exploits users' historical check-ins and other multi-modal information such as POI attributes and friendship network, to recommend the next set of POIs suitable for a user. A plethora of earlier works focused on traditional machine learning techniques by using hand-crafted features from the dataset. With the recent surge of deep learning research, we have witnessed a large variety of POI recommendation works utilizing different deep learning paradigms. These techniques largely vary in problem formulations, proposed techniques, used datasets, and features, etc. To the best of our knowledge, this work is the first comprehensive survey of all major deep learning-based POI recommendation works. Our work categorizes and critically analyzes the recent POI recommendation works based on different deep learning paradigms and other relevant features. This review can be considered a cookbook for researchers or practitioners working in the area of POI recommendation.