Sage Journals: Discover world-class research

Abstract

As things in IoT keep evolving, people's choices for what to use are influenced by their situation and can vary which calls for systems that can adapt to these changes. Here, a framework is described that uses suggestions from collaborative filtering, content-based filtering, and a deep neural network in partnership with ensemble learning. Realistically, it makes use of matrix factorization, a k-nearest neighbors’ approach, and a convolutional neural network (CNN) to organize its model. For example, by using CASAS and SmartThings data that is made open to the public, the model is trained and its performance tested by focusing on timing elements, recordings of user-device interaction and context details. In evaluation, precision, recall, F1-score, novelty, and context precision are used, helping to analyse a system from several angles. Matrix factorization and deep learning models cannot beat them, as they achieved a better accuracy (+8.5%), created more innovative recommendations (+12.3%) and operated faster on average (average latency was reduced by 27%). The architecture is equipped to handle privacy issues and is flexible to fit within edge computing. Charts and diagrams are included to demonstrate the design and how different components connect.

Keywords

Internet of Things recommender systems hybrid machine learning real-Time recommendations collaborative filtering content-Based filtering deep learning user-centric IoT data overload adaptive systems

Get full access to this article

View all access options for this article.

References

Akbar

M. A.

Hassan

M. M.

Hossain

M. S.

(2018). Lightweight recommendation algorithms for resource-constrained devices. Computers and Electrical Engineering, 69, 48–61.

Chen

Xiao

(2021). Real-time recommendation in IoT environments using reinforcement learning. IEEE Internet of Things Journal.

Doshi-Velez

Kim

(2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.

Fang

Chen

(2021). Edge-based IoT architecture for real-time data processing. Sensors, 21(9), 3031. https://doi.org/10.3390/s21093031

Liao

Zhang

Nie

Chua

T. S.

(2017). Neural collaborative filtering. Proceedings of the 26th international conference on world wide web (pp. 173–182).

Zhang

Kan

M. Y.

Chua

T. S.

(2018). Outer product-based neural collaborative filtering. IJCAI, 2018, 2227–2233. https://doi.org/10.24963/ijcai.2018/308

Khan

(2022). Federated hybrid recommender systems for healthcare IoT: A privacy-focused approach. IEEE Access, 10, 99602–99615.

Kipf

T. N.

Welling

(2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.

Koren

Bell

Volinsky

(2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37. https://doi.org/10.1109/MC.2009.263

10.

Sun

Wang

(2020). Reinforcement learning in dynamic recommendation systems. IEEE Access, 8, 120–129.

11.

Liu

Zhang

(2022). Hybrid reinforcement-based recommender systems. Knowledge-based Systems, 250.

12.

Maheswari

K. G.

Nagappan

Siva

(2024). Enhancing intelligent transport systems: A cutting-edge framework for context-aware service management with hybrid deep learning. Simulation Modelling Practice and Theory, 135. https://doi.org/10.1016/j.simpat.2024.102934

13.

McMahan

H. B.

Moore

Ramage

Hampson

(2017) Communication-efficient learning of deep networks from decentralized data. The Proceedings of AISTATS.

14.

Rahman

(2023). Cross-domain recommender adaptation for edge-based IoT environments. Future Generation Computer Systems.

15.

Raza

(2023). Smart aquaculture analytics: Enhancing shrimp farming in Bangladesh through real-time IoT monitoring and predictive machine learning analysis. Heliyon, 9(4), e12345.

16.

Sarwar

(2021). A hybrid recommendation framework with device optimization for IoT. Future Generation Computer Systems, 116, 546–559.

17.

Shi

Cao

Zhang

(2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646. https://doi.org/10.1109/JIOT.2016.2579198

18.

Sun

Zhu

Chen

(2021). Hybrid deep neural network models for recommendation systems. IEEE Access, 9, 349–356. https://doi.org/10.1109/ACCESS.2020.3046719

19.

Varghese

N. N.

Jose

Bindhumol

Cleetus

(2025). The power duo: Unleashing cognitive potential through human-AI synergy in STEM and non-STEM education. Frontiers in Education, 25(2), 48–52. https://www.frontiersin.org/articles/10.3389/feduc.2025.1534582/full. https://doi.org/10.3389/feduc.2025.1534582

20.

Vaswani

Shazeer

Parmar

Uszkoreit

Jones

Gomez

A. N.

Kaiser

Polosukhin

(2017). Attention is all you need. Advances in neural information processing systems (NeurIPS).

21.

Velliangiri

Damle

Abraham

P. S. A.

(2025). Dynamic trust-based access control with hybrid encryption for secure IoT applications. International Journal of Computing Sciences Research.

22.

Wang

Cheng

Shen

Liu

(2019). Hybrid recommender systems for dynamic environments. Knowl-Based Syst, 176, 61–75. https://doi.org/10.1016/j.knosys.2019.03.028

23.

Pan

Long

Jiang

Zhang

(2020). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems.

24.

Yang

Liu

Chen

Tong

(2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology, 10(2), 1–19. https://doi.org/10.1145/3298981

25.

Yin

Chen

Guo

(2020). Scalable hybrid models for IoT recommendation systems. IEEE Internet of Things Journal, 7(9), 9021–9032.

26.

Zaharia

Chowdhury

Das

Dave

McCauly

Franklin

M. J.

Shenker

Stoica

(2013). Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. 9th USENIX symposium on networked systems design and implementation (NSDI 12).

27.

Zhang

(2020). Enhanced collaborative filtering with deep hybrid models. IEEE Transactions on Neural Networks and Learning Systems.

A Context-Aware Hybrid Recommendation Framework for Real-Time IoT Environments

Abstract

Keywords

Get full access to this article

References