# Topic-Enhanced Memory Networks for Personalised Point-of-Interest Recommendation

## TL;DR

This paper introduces a novel deep learning model called Topic-Enhanced Memory Network (TEMN) that improves personalized POI recommendations by capturing complex user preferences and spatial influences, outperforming existing methods.

## Contribution

The paper proposes a hybrid deep architecture combining topic modeling and memory networks, incorporating spatial modules for enhanced POI recommendation accuracy.

## Key findings

- Improves context-aware recommendation accuracy by 3.25%.
- Enhances sequential recommendation performance by 29.95%.
- Provides interpretability through attention weights and topic analysis.

## Abstract

Point-of-Interest (POI) recommender systems play a vital role in people's lives by recommending unexplored POIs to users and have drawn extensive attention from both academia and industry. Despite their value, however, they still suffer from the challenges of capturing complicated user preferences and fine-grained user-POI relationship for spatio-temporal sensitive POI recommendation. Existing recommendation algorithms, including both shallow and deep approaches, usually embed the visiting records of a user into a single latent vector to model user preferences: this has limited power of representation and interpretability. In this paper, we propose a novel topic-enhanced memory network (TEMN), a deep architecture to integrate the topic model and memory network capitalising on the strengths of both the global structure of latent patterns and local neighbourhood-based features in a nonlinear fashion. We further incorporate a geographical module to exploit user-specific spatial preference and POI-specific spatial influence to enhance recommendations. The proposed unified hybrid model is widely applicable to various POI recommendation scenarios. Extensive experiments on real-world WeChat datasets demonstrate its effectiveness (improvement ratio of 3.25% and 29.95% for context-aware and sequential recommendation, respectively). Also, qualitative analysis of the attention weights and topic modeling provides insight into the model's recommendation process and results.