BERT4Loc: BERT for Location—POI Recommender System

Syed Raza Bashir, Shaina Raza and Vojislav B. Misic
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Syed Raza Bashir: Department of Computer Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
Shaina Raza: Vector Institute of Artificial Intelligence, Toronto, ON M5G 1M1, Canada
Vojislav B. Misic: Department of Computer Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada

Future Internet, 2023, vol. 15, issue 6, 1-19

Abstract: Recommending points of interest (POI) is a challenging task that requires extracting comprehensive location data from location-based social media platforms. To provide effective location-based recommendations, it is important to analyze users’ historical behavior and preferences. In this study, we present a sophisticated location-aware recommendation system that uses Bidirectional Encoder Representations from Transformers (BERT) to offer personalized location-based suggestions. Our model combines location information and user preferences to provide more relevant recommendations compared to models that predict the next POI in a sequence. Based on our experiments conducted on two benchmark datasets, we have observed that our BERT-based model surpasses baselines models in terms of HR by a significant margin of 6% compared to the second-best performing baseline. Furthermore, our model demonstrates a percentage gain of 1–2% in the NDCG compared to second best baseline. These results indicate the superior performance and effectiveness of our BERT-based approach in comparison to other models when evaluating HR and NDCG metrics. Moreover, we see the effectiveness of the proposed model for quality through additional experiments.

Keywords: points of interest; BERT; transformer; deep neural network; recommender systems (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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