Memory efficient location recommendation through proximity-aware representation

TL;DR

This paper introduces PASR, a proximity-aware region representation for sequential location recommendation that effectively addresses data sparsity and enhances geographic information utilization using self-attention mechanisms.

Contribution

The paper presents a novel proximity-aware region representation and a loss function with importance sampling, improving location recommendation accuracy and geographic information integration.

Findings

PASR outperforms state-of-the-art methods on real-world datasets.

The proximity-aware loss improves negative sampling quality.

Self-attention enhances geographic information encoding.

Abstract

Sequential location recommendation plays a huge role in modern life, which can enhance user experience, bring more profit to businesses and assist in government administration. Although methods for location recommendation have evolved significantly thanks to the development of recommendation systems, there is still limited utilization of geographic information, along with the ongoing challenge of addressing data sparsity. In response, we introduce a Proximity-aware based region representation for Sequential Recommendation (PASR for short), built upon the Self-Attention Network architecture. We tackle the sparsity issue through a novel loss function employing importance sampling, which emphasizes informative negative samples during optimization. Moreover, PASR enhances the integration of geographic information by employing a self-attention-based geography encoder to the hierarchical grid and proximity grid at each GPS point. To further leverage geographic information, we utilize the proximity-aware negative samplers to enhance the quality of negative samples. We conducted evaluations using three real-world Location-Based Social Networking (LBSN) datasets, demonstrating that PASR surpasses state-of-the-art sequential location recommendation methods