Acta Informatica Pragensia 2024, 13(1), 38-61 | DOI: 10.18267/j.aip.2283716

Dynamic Context-Aware Recommender System for Home Automation Through Synergistic Unsupervised and Supervised Learning Algorithms

Tahar Dilekh ORCID..., Saber Benharzallah ORCID..., Ayoub Mokeddem ORCID..., Saoueb Kerdoudi ORCID...
Department of Computer Science, University of Batna 2, Batna, Algeria

Home automation, supported by smart devices and the internet of things, works to enhance household control. However, the reliance on current systems with fixed rules poses challenges, which can be inflexible and anxiety-provoking for users who want control over their smart home devices, limit responsiveness to changing conditions and affect energy efficiency, comfort and security. To address this, the paper proposes a dynamic personalized recommender system that considers the user's current state and contextual preferences to suggest relevant automation services for smart home devices. The system uses an unsupervised algorithm to extract rules from past interactions and supervised algorithms to make recommendations based on those rules. The proposed context-aware recommender system for smart homes achieved a remarkable average accuracy of 86.99%, a recall of 76.06% and a precision of 82.67% on publicly available datasets, surpassing previous studies. It offers users an enhanced quality of life, energy efficiency and cost reduction, while providing service providers with increased engagement and valuable insights.

Keywords: Association rule; Generalized linear model; Machine learning; Predictive models; Recommender systems; Context-aware services; Home automation.

Received: September 19, 2023; Revised: December 13, 2023; Accepted: December 18, 2023; Prepublished online: January 16, 2024; Published: April 15, 2024  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Dilekh, T., Benharzallah, S., Mokeddem, A., & Kerdoudi, S. (2024). Dynamic Context-Aware Recommender System for Home Automation Through Synergistic Unsupervised and Supervised Learning Algorithms. Acta Informatica Pragensia, 13(1), 38-61. doi: 10.18267/j.aip.228
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proc. 20th Int. Conf. Very Large Data Bases, (pp. 487-499). VLDB. https://www.vldb.org/conf/1994/P487.PDF
    2. Aldino, A. A., Pratiwi, E. D., Setiawansyah, Sintaro, S., & Dwi Putra, A. (2021). Comparison Of Market Basket Analysis To Determine Consumer Purchasing Patterns Using Fp-Growth And Apriori Algorithm. In 2021 International Conference on Computer Science, Information Technology, and Electrical Engineering (ICOMITEE), (pp. 29-34). IEEE. https://doi.org/10.1109/ICOMITEE53461.2021.9650317 Go to original source...
    3. Aldrich, F. K. (2003). Smart Homes: Past, Present and Future. In Inside the Smart Home (pp. 17-39). Springer-Verlag. https://doi.org/10.1007/1-85233-854-7_2 Go to original source...
    4. Ali, S. M. M., Augusto, J. C., Windridge, D., & Ward, E. (2023). A user-guided personalization methodology to facilitate new smart home occupancy. Universal Access in the Information Society, 22(3), 869-891. https://doi.org/10.1007/s10209-022-00883-x Go to original source...
    5. Alshammari, T., Alshammari, N., Sedky, M., & Howard, C. (2018). SIMADL: Simulated Activities of Daily Living Dataset. Data, 3(2), 11. https://doi.org/10.3390/data3020011 Go to original source...
    6. Azevedo, P. J., & Jorge, A. M. (2007). Comparing rule measures for predictive association rules. In European Conference on Machine Learning, (pp. 510-517). https://doi.org/10.1007/978-3-540-74958-5_47 Go to original source...
    7. Belaidouni, S., Miraoui, M., & Tadj, C. (2022). QL-CBR Hybrid Approach for Adapting Context-Aware Services. Computer Systems Science and Engineering, 43(3), 1085-1098. https://doi.org/10.32604/csse.2022.024056 Go to original source...
    8. Belghini, N., Gouttaya, N., Bouab, W., & Sayouti, A. (2016). Pervasive Recommender System for Smart Home Environment. International Journal of Applied Information Systems, 10(9), 1-7. https://doi.org/10.5120/ijais2016451528 Go to original source...
    9. Brush, A. J. B., Lee, B., Mahajan, R., Agarwal, S., Saroiu, S., & Dixon, C. (2011). Home automation in the wild: Challenges and opportunities. In Conference on Human Factors in Computing Systems - Proceedings, (pp. 2115-2124). ACM. https://doi.org/10.1145/1978942.1979249 Go to original source...
    10. Davidoff, S., Lee, M. K., Yiu, C., Zimmerman, J., & Dey, A. K. (2006). Principles of Smart Home Control. In UbiComp 2006: Ubiquitous Computing: 8th International Conference, (pp. 19-34). Springer. https://doi.org/10.1007/11853565_2 Go to original source...
    11. Eggen, B., Hollemans, G., & van de Sluis, R. (2003). Exploring and enhancing the home experience. Cognition, Technology & Work, 5(1), 44-54. https://doi.org/10.1007/s10111-002-0114-7 Go to original source...
    12. Engelmann, K. F., Holthaus, P., Wrede, B., & Wrede, S. (2016). An interaction-centric dataset for learning automation rules in smart homes. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), (pp. 1432-1437). European Language Resources Association. https://aclanthology.org/L16-1228/
    13. Gupta, S. (2020). A context-aware personalized recommender system for automation in IoT based smart home environment. Master thesis. Dublin Business School. https://esource.dbs.ie/handle/10788/4240
    14. Han, J., Pei, J., Yin, Y., & Mao, R. (2004). Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery, 8, 53-87. https://doi.org/10.1023/B:DAMI.0000005258.31418.83 Go to original source...
    15. Hunyadi, D. (2011). Performance comparison of Apriori and FP-Growth algorithms in generating association rules. In Proceedings of the 5th European Conference on European Computing Conference, (pp. 376-381). ACM.
    16. Kotu, V., & Deshpande, B. (2014). Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner. Morgan Kaufmann. Go to original source...
    17. Leonidis, A., Korozi, M., Kouroumalis, V., Poutouris, E., Stefanidi, E., Arampatzis, D., Sykianaki, E., Anyfantis, N., Kalligiannakis, E., Nicodemou, V. C., Stefanidi, Z., Adamakis, E., Stivaktakis, N., Evdaimon, T., & Antona, M. (2019). Ambient intelligence in the living room. Sensors, 19(22), 5011. https://doi.org/10.3390/s19225011 Go to original source...
    18. Mennicken, S., & Huang, E. M. (2012). Hacking the natural habitat: An in-the-wild study of smart homes, their development, and the people who live in them. In Pervasive Computing 2012, (pp. 143-160). Springer. https://doi.org/10.1007/978-3-642-31205-2_10 Go to original source...
    19. Miraoui, M. (2018). A Context-Aware Smart Office for Improved Comfort and Energy Saving. In Intelligent Environments 2018, (pp. 455-465). IOS Press. https://doi.org/10.3233/978-1-61499-874-7-455 Go to original source...
    20. Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized Linear Models. Journal of the Royal Statistical Society. Series A, 135(3), 370-384. https://doi.org/10.2307/2344614 Go to original source...
    21. Ortiz-Barrios, M., Järpe, E., García-Constantino, M., Cleland, I., Nugent, C., Arias-Fonseca, S., & Jaramillo-Rueda, N. (2022). Predicting Activity Duration in Smart Sensing Environments Using Synthetic Data and Partial Least Squares Regression: The Case of Dementia Patients. Sensors, 22(14), 5410. https://doi.org/10.3390/s22145410 Go to original source...
    22. Osman, K., ©tefić, M., Alajbeg, T., & Perić, M. (2023). Approach in development and implementation of automatic control system in a smart building. Energy and Buildings, 293, 113200. https://doi.org/10.1016/j.enbuild.2023.113200 Go to original source...
    23. Ramesh, P. N., & Kannimuthu, S. (2023). Context-Aware Practice Problem Recommendation Using Learners' Skill Level Navigation Patterns. Intelligent Automation and Soft Computing, 35(3), 3845-3860. https://doi.org/10.32604/iasc.2023.031329 Go to original source...
    24. Randall, D. (2003). Living Inside a Smart Home: A Case Study. In Inside the Smart Home (pp. 227-246). Springer-Verlag. https://doi.org/10.1007/1-85233-854-7_12 Go to original source...
    25. Rasch, K. (2014). An unsupervised recommender system for smart homes. Journal of Ambient Intelligence and Smart Environments, 6(1), 21-37. https://doi.org/10.3233/AIS-130242 Go to original source...
    26. Reyes-Campos, J., Alor-Hernández, G., Machorro-Cano, I., Olmedo-Aguirre, J. O., Sánchez-Cervantes, J. L., & Rodríguez-Mazahua, L. (2021). Discovery of Resident Behavior Patterns Using Machine Learning Techniques and IoT Paradigm. Mathematics, 9(3), 219. https://doi.org/10.3390/math9030219 Go to original source...
    27. Tan, P.-N., Steinbach, M., & Kumar, V. (2005). Introduction to Data Mining. Wesley Longman.
    28. Van Kasteren, T. L. M., Englebienne, G., & Kröse, B. J. A. (2010). Transferring knowledge of activity recognition across sensor networks. In Pervasive Computing, (pp. 283-300). Springer. https://doi.org/10.1007/978-3-642-12654-3_17 Go to original source...
    29. Van Kasteren, T., Noulas, A., Englebienne, G., & Kröse, B. (2008). Accurate activity recognition in a home setting. In UbiComp 2008 - Proceedings of the 10th International Conference on Ubiquitous Computing, (pp. 1-9). ACM. https://doi.org/10.1145/1409635.1409637 Go to original source...
    30. Verkasalo, H., & Karjalainen, J. (2010). System and method for behavioural and contextual data analytics. International patent, https://patents.google.com/patent/WO2010128198A1/ja
    31. Verma, R. M., & Chen, P. (2012). A data mining hypertextbook: Design, implementation and experience. Journal of Computing Sciences in Colleges, 27(3), 22-28.
    32. Woodruff, A., Augustin, S., & Foucault, B. (2007). Sabbath day home automation. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, (pp. 527-536). ACM. https://doi.org/10.1145/1240624.1240710 Go to original source...
    33. Zheng, B., & Agresti, A. (2000). Summarizing the predictive power of a generalized linear model. Statistics in Medicine, 19(13), 1771-1781. https://doi.org/10.1002/1097-0258(20000715)19:13%3C1771::AID-SIM485%3E3.0.CO;2-P Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content