Next location prediction using transformers
by Salah Eddine Henouda; Fatima Zohra Laallam; Okba Kazar; Abdessamed Sassi
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 21, No. 2, 2022

Abstract: This work seeks to solve the next location prediction problem of mobile users. Chiefly, we focus on ROBERTA architecture (robustly optimised BERT approach) in order to build a next location prediction model through the use of a subset of a large real mobility trace database. The latter was made available to the public through the CRAWDAD project. ROBERTA, which is a well-known model in natural language processing (NLP), works intentionally on predicting hidden sections of text based on language masking strategy. The current paper follows a similar architecture as ROBERTA and proposes a new combination of Bertwordpiece tokeniser and ROBERTA for location prediction that we call WP-BERTA. The results demonstrated that our proposed model WP-BERTA outperformed the state-of-the-art models. They also indicated that the proposed model provided a significant improvement in the next location prediction accuracy compared to the state-of-the-art models. We particularly revealed that WP-BERTA outperformed Markovian models, support vector machine (SVM), convolutional neural networks (CNNs), and long short-term memory networks (LSTMs).

Online publication date: Thu, 11-Aug-2022

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