2022/08/02 by Syed Raza Bashir, Bashir, Syed Raza, Raza, Shaina +1
Business, Management and Accounting · Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Information Retrieval (cs.IR) #Recommender Systems and Techniques #Sharing Economy and Platforms
paper · pdf · doi:10.48550/arxiv.2208.01375
openalex publication_date 2022/08/02 · openalex created_date 2023/06/15 · openalex updated_date 2026/07/28
Recommending points of interest (POIs) is a challenging task that requires extracting comprehensive location data from location-based social media platforms. To provide effective location-based recommendations, it's 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. Our experiments on two benchmark dataset show that our BERT-based model outperforms various state-of-the-art sequential models. Moreover, we see the effectiveness of the proposed model for quality through additional experiments.