CARAN: A Context-Aware Recency-Based Attention Network for Point-of-Interest Recommendation

Md Billal Hossain, Mohammad Shamsul Arefin*, Iqbal H. Sarker, Md Kowsher, Pranab Kumar Dhar, Takeshi Koshiba

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Point-of-interest (POI) recommendation system that tries to anticipate user's next visiting location has attracted a plentiful research interest due to its ability in generating personalized suggestions. Since user's historical check-ins are sequential in nature, Recurrent neural network (RNN) based models with context embedding shows promising result for modeling user's mobility. However, such models cannot provide a correlation between non-consecutive and non-adjacent visits for understanding user's behavior. To mitigate data sparsity problem, many models use hierarchical gridding of the map which cannot represent spatial distance smoothly. Another important factor while providing POI recommendation is the impact of weather conditions which has rarely been considered in the literature. To address the above shortcomings, we propose a Context-Aware Recency based Attention Network (CARAN) that incorporates weather conditions with spatiotemporal context and gives focus on recently visited locations using the attention mechanism. It allows interaction between non-adjacent check-ins by using spatiotemporal matrices and uses linear interpolation for smooth representation of spatial distance. Moreover, we use positional encoding of the check-in sequence in order to maintain relative position of the visited locations. We evaluate our proposed model on three real world datasets and the result shows that CARAN surpasses the existing state-of-the art models by 7-14%.

Original languageEnglish
Pages (from-to)36299-36310
Number of pages12
JournalIEEE Access
Publication statusPublished - 2022


  • Attention model
  • location based services
  • point-of-interest
  • recommendation system
  • spatio-temporal

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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