# RELINE: Point-of-Interest Recommendations using Multiple Network Embeddings

## TL;DR

RELINE is a unified embedding model that integrates social, geographical, temporal, and preference factors to improve POI recommendation accuracy and address cold-start issues in Location-Based Social Networks.

## Contribution

It introduces a novel multi-network embedding approach that jointly models various influences for enhanced POI recommendation.

## Key findings

- Significant accuracy improvements over state-of-the-art methods.
- Effective in mitigating cold-start problems.
- Validated on large real-world datasets.

## Abstract

The rapid growth of users' involvement in Location-Based Social Networks (LBSNs) has led to the expeditious growth of the data on a global scale. The need of accessing and retrieving relevant information close to users' preferences is an open problem which continuously raises new challenges for recommendation systems. The exploitation of Points-of-Interest (POIs) recommendation by existing models is inadequate due to the sparsity and the cold start problems. To overcome these problems many models were proposed in the literature, but most of them ignore important factors such as: geographical proximity, social influence, or temporal and preference dynamics, which tackle their accuracy while personalize their recommendations. In this work, we investigate these problems and present a unified model that jointly learns users and POI dynamics. Our proposal is termed RELINE (REcommendations with muLtIple Network Embeddings). More specifically, RELINE captures: i) the social, ii) the geographical, iii) the temporal influence, and iv) the users' preference dynamics, by embedding eight relational graphs into one shared latent space. We have evaluated our approach against state-of-the-art methods with three large real-world datasets in terms of accuracy. Additionally, we have examined the effectiveness of our approach against the cold-start problem. Performance evaluation results demonstrate that significant performance improvement is achieved in comparison to existing state-of-the-art methods.