Location Prediction via Bi-direction Speculation and Dual-level Association

TL;DR

This paper introduces BSDA, a novel location prediction model that leverages bi-direction speculation and dual-level association to address data sparsity and improve prediction accuracy by considering user interests and POI appeal.

Contribution

The paper proposes BSDA, a new method combining bi-direction speculation and dual-level association to enhance location prediction, especially under data sparsity conditions.

Findings

BSDA outperforms existing models on public datasets.

Incorporating cross-user and cross-POI associations improves prediction accuracy.

The method effectively mitigates data sparsity issues in location prediction.

Abstract

Location prediction is of great importance in location-based applications for the construction of the smart city. To our knowledge, existing models for location prediction focus on users' preferences on POIs from the perspective of the human side. However, modeling users' interests from the historical trajectory is still limited by the data sparsity. Additionally, most of existing methods predict the next location according to the individual data independently, but the data sparsity makes it difficult to mine explicit mobility patterns or capture the casual behavior for each user. To address the issues above, we propose a novel Bi-direction Speculation and Dual-level Association method (BSDA), which considers both users' interests in POIs and POIs' appeal to users. Furthermore, we develop the cross-user and cross-POI association to alleviate the data sparsity by similar users and POIs to enrich the candidates. Experimental results on two public datasets demonstrate that BSDA achieves significant improvements over state-of-the-art methods.