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An AI-driven social media recommender system leveraging smartphone and IoT data

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ACorrection to this article was published on 09 January 2025

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Abstract

Our research presents “RoBERTaRecIOT,” an innovative model that stands out for its superiority. It utilizes the pre-trained Robustly Optimized Bidirectional Encoder Representations from Transformers (RoBERTa) framework to deliver personalized travel recommendations via social media. This model is not just a theoretical concept but a practical solution, proposing an advanced travel route recommendation system that automatically gathers tourists’ onsite behavioral data related to specific POIs, utilizing a smartphone and the Internet of Things (IoT) technologies. It surpasses traditional sequential prediction barriers by integrating bidirectional context, non-symmetric schemas, and sophisticated enhancing user similarity evaluations through topic modeling. Furthermore, we introduce a new preference assessment method employing explicit demographic information, significantly mitigating the effects of the cold start issue. Our empirical studies, utilizing Yelp and Flickr datasets, demonstrate the model’s superiority, surpassing conventional metrics with improved F-Score, MAP, and NDCG values. The “RoBERTaRecIOT” model stands out as a revolutionary instrument in the domain of social media-driven travel recommendations, providing a dynamic and user-focused experience for global adventurers.

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Data availability

No datasets were generated or analyzed during the current study.

Change history

  • 21 December 2024

    The original online version of this article was revised: Xiaoyu Zhou should also be marked as a corresponding author.

  • 09 January 2025

    A Correction to this paper has been published:https://doi.org/10.1007/s11227-024-06857-5

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Funding

This work was supported by 2023 Henan Province Science and Technology Research (Grant No. 232102310506), the Science and Technology Foundation of Henan Province of China (Grant No. 222102210250), the Research on Teaching Reform of Henan Polytechnic (Grant No. 2021J058), the Scientific Research of Henan Polytechnic (Grant No. 2022ZK49), and Teaching Reform and Practice Program of Vocational Education in Henan Province (Grant No. [2023] 03049).

Author information

Authors and Affiliations

  1. College of Modern Information Technology, Henan Polytechnic, Zhengzhou, 450046, Henan, China

    Dongxian Yu

  2. School of Management, Wuhan Donghu University, Wuhan, 430000, Hubei, China

    Xiaoyu Zhou

  3. School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA

    Ali Noorian

  4. Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, V8W 2Y2, Canada

    Mehdi Hazratifard

Authors
  1. Dongxian Yu
  2. Xiaoyu Zhou
  3. Ali Noorian
  4. Mehdi Hazratifard

Contributions

Dongxian Yu, Xiaoyu Zhou, Ali Noorian, and Mehdi Hazratifard wrote the main manuscript text. All authors reviewed the manuscript.

Corresponding authors

Correspondence toXiaoyu Zhou orAli Noorian.

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Conflict of interest

The authors declare no competing interests.

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The original online version of this article was revised: Xiaoyu Zhou should also be marked as a corresponding author.

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