A Recommender System Using a Support Vector Machine and the TOPSIS Model in the Internet of Things

Document Type : Original Article

Authors

1 Assistant Professor, Technical and Engineering Faculty, Islamic Azad University, Ayatollah Amoly branch, Amoly, Iran.

2 Master's student, Islamic Azad University, Ayatollah Amoli Unit, Amol, Iran.

Abstract

The Internet of Things is an emerging information architecture based on the Internet that develops interaction between things and services in a safe and reliable environment. In fact, the purpose of this structure is to reduce the distance between the things of the physical world and information systems. In the Internet of Things, it is expected that intelligent devices will become active members in business and informational and social processes, so that they are able to interact between themselves and the external environment through the exchange of data and sensed information. In fact, the Internet of Things is a network of devices in which various things can communicate with other equipment with the help of computers and through Internet connections.  Recommendation technologies can help to more easily identify relevant artifacts and thus will become one of the key technologies in future IoT solutions.  The main task of recommender systems is to recommend service providers that meet the different needs of users. The paper porposes a Support Vector Machine (SVM) based algorithm and the TOPSIS multi-criteria decision-making model in order to create an effective recommender system and provide suggestions to users based on their preferences and increase user satisfaction. The experimental results show that the proposed recommender system can produce a series of objective recommendations that are effective based on accuracy and variety, novelty and high coverage. Finally, the results confirm the improvement in making recommendations .
 

Keywords

Smiley face

https://creativecommons.org/licenses/by/4.0/ This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0) CopyRight (C) Authors

References

[1]     A. Mattioli, and F.  Patern, “A Visual Environment for End-User Creation of IoT Customization Rules with Recommendation Support,” In Proceedings of the International Conference on Advanced Visual Interfaces, New York, NY, USA: Association for Computing Machinery, 2020, pp.1-5.
[2]     A.S. Devasthali, et al., “IoT Based Inventory Management System with Recipe Recommendation Using Collaborative Filtering,” Evolutionary Computing and Mobile Sustainable Networks, Vol. 53, pp. 543–550, August 2020.
[3]     A. Gyrard,  and S. Amit, “IAMHAPPY: Towards an IoT Knowledge-Based Cross-Domain Well-Being Recommendation System for Everyday Happiness,” Smart Health, Vol. 15, pp. 100-118, March 2020.
[4]     S. Beg, et al., “A Privacy-Preserving Protocol for Continuous and Dynamic Data Collection in IoT Enabled Mobile App Recommendation System (MARS),” J. Netw. Comput. Appl, Vol. 174, pp. 102-124, January 2021.
[5]     I. Mashal, T. -Y. Chung and O. Alsaryrah, "Toward service recommendation in Internet of Things," 2015 Seventh International Conference on Ubiquitous and Future Networks, 2015, pp. 328-331. 
[6]     N. Sachdeva, R.  Dhir, and A. Kumar,  “Empirical Analysis of Machine Learning Techniques for Context Aware Recommender Systems in the Environment of IoT,” In Proceedings of the International Conference on Advances in Information Communication Technology & Computing, AICTC ’16, 2016,  pp. 1–7.
[7]     J. Pashaei, S. Yousefi, and B. Masoum, “Efficient Service Recommendation Using Ensemble Learning in the Internet of Things (IoT),” J Ambient Intell Humaniz Comput, Vol. 11, No. 3, pp. 1339–1350, 2020.
[8]     B. Cao, et al.,  “QoS-Aware Service Recommendation Based on Relational Topic Model and Factorization Machines for IoT Mashup Applications,” J Parallel Distrib Comput , Vol. 132, pp. 177–189, October 2019.
[9]     S. Di Martino, and S. Rossi, “An Architecture for a Mobility Recommender System in Smart Cities,” Procedia Comput. Sci., Vol. 98, pp. 425–430, 2016.
[10]  S. Forouzandeh, et al., “Recommender system for Users of Internet of Things (IOT),” Int. J. Netw. Secur, Vol. 17, No. 4, pp. 47-56, 2017.
[11]  H. Jeong, et al., “Big Data and Rule-Based Recommendation System in Internet of Things,” Cluster Comput, Vol. 22, pp. 1837–184, 2019.
[12]  B. Twardowski and D. Ryzko, "IoT and Context-Aware Mobile Recommendations Using Multi-agent Systems," 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2015, pp. 33-40.
[13]  X. Wen, “Using Deep Learning Approach and IoT Architecture to Build the Intelligent Music Recommendation System,” Soft Comput, Vol. 25, pp. 3087–3096, October 2020
[14]  X. Cai, et al., “A Hybrid Recommendation System with Many-Objective Evolutionary Algorithm,” Expert Syst. Appl, Vol. 159, pp. 113-148, November 2020.
[15]  W. Gong, et al., “Diversified and Compatible Web APIs Recommendation in IoT,” arXiv:2107.10538 [cs], 2021
[16]  M. Lihong, Z. Yanping and Z. Zhiwei, "Improved VIKOR Algorithm Based on AHP and Shannon Entropy in the Selection of Thermal Power Enterprise's Coal Suppliers," 2008 International Conference on Information Management, Innovation Management and Industrial Engineering, 2008, pp. 129-133. 
 

Files

History

  • Receive Date: 13 October 2023
  • Revise Date: 10 December 2023
  • Accept Date: 23 December 2023
  • Publish Date: 18 January 2024

Share

How to cite

Statistics