e-ISSN 2231-8526
ISSN 0128-7680

Home / Regular Issue / JST Vol. 29 (4) Oct. 2021 / JST-2406-2021


An Era of Recommendation Technologies in IoT: Categorisation by techniques, Challenges and Future Scope

Partibha Ahlawat and Chhavi Rana

Pertanika Journal of Science & Technology, Volume 29, Issue 4, October 2021


Keywords: Context-awareness, IoT, knowledge-base, machine learning, recommender system, social IoT

Published on: 29 October 2021

The evolution of the Internet of Things (IoT) accelerates the augmentation of data present on the Internet and possibilities for connections to the more dynamic and heterogeneous devices to the Internet. Recommendation technologies have proven their capabilities of digging the personalised information by proactive filtering in many application domains and can also be a backbone platform in IoT for identifying personalised things, services and relevant artefacts by prevailing over information overload problems. This paper is a comprehensive literature review that categorises IoT recommender systems by exploring the literature’s different IoT based recommendation techniques. We conclude the paper by discussing the challenges and future scope for IoT based recommendations techniques to advancing and widening the frontiers of this research area.

  • Abu-issa, A., Nawawreh, H., Shreteh, L., Salman, Y., Hassouneh, Y., Tumar, I., & Systems, A. R. (2020). A smart city mobile application for multitype, proactive, and context-aware recommender system. In 2017 International Conference on Engineering and Technology (ICET) (pp. 1-5). IEEE Publishing.

  • Aggarwal, C. C. (2016). Recommender systems. Springer International Publishing.

  • Amato, F., Mazzeo, A., Moscato, V., & Picariello, A. (2013). A Recommendation System for Browsing of Multimedia Collections in the Internet. In Internet of things and inter-cooperative computational technologies for collective intelligence (pp. 391-411). Springer.

  • Anthony Jnr, B. (2020). A case-based reasoning recommender system for sustainable smart city development. AI & Society, 36, 159-183.

  • Baltrunas, L., Ludwig, B., & Ricci, F. (2011). Matrix factorization techniques for context aware recommendation. In Proceedings of the fifth ACM conference on Recommender systems (pp. 301-304). Association for Computing Machinery.

  • Barbin, J. P., Yousefi, S., & Masoumi, B. (2020). Efficient service recommendation using ensemble learning in the Internet of things (IoT). Journal of Ambient Intelligence and Humanized Computing, 11(3), 1339-1350.

  • Cao, B., Liu, J., Wen, Y., Li, H., Xiao, Q., & Chen, J. (2019). QoS-aware service recommendation based on relational topic model and factorization machines for IoT Mashup applications. Journal of Parallel and Distributed Computing, 132, 177-189.

  • Cha, S., Ruiz, M. P., Wachowicz, M., Tran, L. H., Cao, H., & Maduako, I. (2017). The role of an IoT platform in the design of real-time recommender systems. In 2016 IEEE 3rd world forum on Internet of things (WF-iot) (pp. 448-453). IEEE Publishing.

  • Chaudhari, S., Azaria, A., & Mitchell, T. (2017). An entity graph based Recommender System. AI Communications, 30(2), 141-149.

  • Chirila, S., Lemnaru, C., & Dinsoreanu, M. (2016). Semantic-based IoT device discovery and recommendation mechanism. In 2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP) (pp. 111-116). IEEE Publishing.

  • Choi, S. M., Lee, H., Han, Y. S., Man, K. L., & Chong, W. K. (2015). A Recommendation Model Using the Bandwagon Effect for E-Marketing Purposes in IoT. International Journal of Distributed Sensor Networks, 11(7), Article 475163.

  • Čolaković, A., & Hadžialić, M. (2018). Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues. Computer Networks, 144, 17-39.

  • Di Martino, S., & Rossi, S. (2016). An architecture for a mobility recommender system in smart cities. Procedia Computer Science, 58, 425-430.

  • Elmisery, A. M., Rho, S., & Sertovic, M. (2017). Privacy aware group based recommender system in multimedia services. Multimedia Tools and Applications, 76(24), 26103-26127.

  • Erdeniz, S. P., Menychtas, A., Maglogiannis, I., Felfernig, A., & Tran, T. N. T. (2019). Recommender systems for IoT enabled quantified-self applications. Evolving Systems, 11(2), 291-304.

  • Felfernig, A., Erdeniz, S. P., Jeran, M., Akcay, A., Azzoni, P., Maiero, M., & Doukas, C. (2017). Recommendation technologies for IoT edge devices. Procedia Computer Science, 110, 504-509.

  • Felfernig, A., Polat-Erdeniz, S., Uran, C., Reiterer, S., Atas, M., Tran, T. N. T., Azzoni, P., Kiraly, C., & Dolui, K. (2019). An overview of recommender systems in the Internet of things. Journal of Intelligent Information Systems, 52(2), 285-309.

  • Forestiero, A. (2017). Multi-Agent recommendation system in Internet of things. In 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) (pp. 772-775). IEEE Publishing.

  • Forouzandeh, S., Aghdam, A. R., Barkhordari, M., & Fahimi, A. (2017). Recommender system for users of Internet of Things (IOT). International Journal of Computer Science and Network Security, 17(8), 46-51.

  • Franco, D. A. I. (2017). A recommender system for automation rules in the Internet of Things (MSc Thesis). Instituto Superior Técnico, Portugal.

  • Frey, R. M., Xu, R., & Ilic, A. (2015). A Novel Recommender System in IoT. In 2015 5th International Conference on the Internet of Things (IOT 2015). IEEE Publishing.

  • Gladence, L. M., Anu, V. M., Rathna, R., & Brumancia, E. (2020). Recommender system for home automation using IoT and artificial intelligence. Journal of Ambient Intelligence and Humanized Computing, 1-9.

  • Guo, Z., & Wang, H. (2020). A deep graph neural network-based mechanism for social recommendations. IEEE Transactions on Industrial Informatics, 3203(c), 1-1.

  • HamlAbadi, K. G., Saghiri, A. M., Vahdati, M., TakhtFooladi, M. D., & Meybodi, M. R. (2018). A framework for cognitive recommender systems in the Internet of Things (IoT). In 2017 IEEE 4th international conference on knowledge-based engineering and innovation (KBEI) (pp. 0971-0976). IEEE Publishing.

  • Huang, Z., Xu, X., Ni, J., Zhu, H., & Wang, C. (2019). Multimodal representation learning for recommendation in Internet of Things. IEEE Internet of Things Journal, 6(6), 10675-10685.

  • Isinkaye, F. O., Folajimi, Y. O., & Ojokoh, B. A. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16(3), 261-273.

  • Iwendi, C., Khan, S., Anajemba, J. H., Bashir, A. K., & Noor, F. (2020). Realizing an efficient IoMT-assisted patient diet recommendation system through machine learning model. IEEE Access, 8, 28462-28474.

  • Jabeen, F., Maqsood, M., Ghazanfar, M. A., Aadil, F., Khan, S., Khan, M. F., & Mehmood, I. (2019). An IoT based efficient hybrid recommender system for cardiovascular disease. Peer-to-Peer Networking and Applications, 12(5), 1263-1276.

  • Kang, D., Choi, H., Choi, S., & Rhee, W. (2017). SRS : Social Correlation Group based Recommender System for Social IoT Environment. International Journal of Contents, 13(1), 53-61.

  • Kang, D., Choi, H., & Rhee, W. (2016). Social Correlation Group Generation Mechanism in Social IoT Environment. In 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 514-519). IEEE Publishing.

  • Kolbe, N., Kubler, S., Robert, J., Le Traon, Y., & Zaslavsky, A. (2019). Linked vocabulary recommendation tools for Internet of things: A survey. ACM Computing Surveys, 51(6), 1-31.

  • Kwon, J., & Kim, S. (2016). Study on Recommendation in Internet of Things Environment. In 2015 7th International Conference on Multimedia, Computer Graphics and Broadcasting (MulGraB) (pp. 13-14). IEEE Publishing.

  • Lee, J. S., & Ko, I. Y. (2016). Service recommendation for user groups in Internet of things environments using member organization-based group similarity measures. In 2016 IEEE international conference on web services (ICWS) (pp. 276-283). IEEE Publishing.

  • Lee, K., Lee, Y. S., & Nam, Y. (2019). A novel approach of making better recommendations by revealing hidden desires and information curation for users of Internet of things. Multimedia Tools and Applications, 78(3), 3183-3201.

  • Lee, K., & Lee, K. (2015). Escaping your comfort zone: A graph-based recommender system for finding novel recommendations among relevant items. Expert Systems with Applications, 42(10), 4851-4858.

  • Mashal, I., Alsaryrah, O., & Chung, T. Y. (2016). Analysis of recommendation algorithms for Internet of Things. In 2016 IEEE Wireless Communications and Networking Conference (pp. 1-6). IEEE Publishing.

  • Mashal, I., Alsaryrah, O., Chung, T. Y., & Yuan, F. C. (2020). A multi-criteria analysis for an Internet of things application recommendation system. Technology in Society, 60, Article 101216.

  • Mashal, I., Chung, T. Y., & Alsaryrah, O. (2015). Toward service recommendation in Internet of Things. In 2015 Seventh International Conference on Ubiquitous and Future Networks (pp. 328-331). IEEE Publishing.

  • Matsui, K., & Choi, H. (2017). A recommendation system with secondary usage of HEMS data for products based on IoT technology. In 2017 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1-6). IEEE Publishing.

  • Milano, S., Taddeo, M., & Floridi, L. (2020). Recommender systems and their ethical challenges. AI & Society, 35(4), 957-967.

  • Mohammadi, V., Rahmani, A. M., Darwesh, A. M., & Sahafi, A. (2019). Trust-based recommendation systems in Internet of Things: a systematic literature review. Human-centric Computing and Information Sciences, 9(1), 1-61.

  • Muñoz-Organero, M., Ramíez-González, G. A., Muñoz-Merino, P. J., & Delgado Kloos, C. (2010). A collaborative recommender system based on space-time similarities. IEEE Pervasive Computing, 9(3), 81-87.

  • Musto, C., Lops, P., Basile, P., de Gemmis, M., & Semeraro, G. (2016). Semantics-aware graph-based recommender systems exploiting linked open data. In Proceedings of the 2016 conference on user modeling adaptation and personalization (pp. 229-237). Association for Computing Machinery.

  • Nizamkari, N. S. (2017). A graph-based trust-enhanced recommender system for service selection in IOT. In 2017 International Conference on Inventive Systems and Control (ICISC) (pp. 1-5). IEEE Publishing.

  • Noirie, L., Le Pallec, M., & Ammar, N. (2017). Towards automated IoT service recommendation. In 2017 20th Conference on Innovations in Clouds, Internet and Networks (ICIN) (pp. 103-106). IEEE Publishing.

  • Ouhbi, B., Frikh, B., Zemmouri, E., & Abbad, A. (2018). Deep learning based recommender system. In 2018 IEEE 5th International Congress on Information Science and Technology (CiSt) (pp. 161-166).

  • Palaiokrassas, G., Karlis, I., Litke, A., Charlaftis, V., & Varvarigou, T. (2017). An IoT architecture for personalized recommendations over big data oriented applications. In 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC) (Vol. 2, pp. 475-480). IEEE Publishing.

  • Pratibha, & Kaur, P. D. (2018). Towards incorporating context awareness to recommender systems in Internet of things. Smart Innovation, Systems and Technologies, 79, 771-780.

  • Ravi, L., Vairavasundaram, S., Palani, S., & Devarajan, M. (2019). Location-based personalized recommender system in the Internet of cultural things. Journal of Intelligent & Fuzzy Systems, 36(5), 4141-4152.

  • Roopa, M. S., Pattar, S., Buyya, R., Venugopal, K. R., Iyengar, S. S., & Patnaik, L. M. (2019). Social Internet of Things (SIoT): Foundations, thrust areas, systematic review and future directions. Computer Communications, 139(September 2018), 32-57.

  • Sawant, S. D., Sonawane, K. V., Jagani, T., & Chaudhari, A. N. (2017). Representation of recommender system in IoT using cyber physical techniques. In 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA) (Vol. 2, pp. 372-375). IEEE Publishing.

  • Saghiri, A. M., Vahdati, M., Gholizadeh, K., Meybodi, M. R., Dehghan, M., & Rashidi, H. (2018). A framework for cognitive Internet of Things based on blockchain. In 2018 4th International Conference on Web Research (ICWR) (pp. 138-143). IEEE Publishing.

  • Saleem, Y., Crespi, N., Rehmani, M. H., Copeland, R., Hussein, D., & Bertin, E. (2017). Exploitation of social IoT for recommendation services. In 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT) (pp. 359-364). IEEE Publishing.

  • Salman, Y., Abu-Issa, A., Tumar, I., & Hassouneh, Y. (2015). A proactive multi-type context-aware recommender system in the environment of Internet of Things. In 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (pp. 351-355). IEEE Publishing.

  • Selvan, N. S., Vairavasundaram, S., & Ravi, L. (2019). Fuzzy ontology-based personalized recommendation for Internet of medical things with linked open data. Journal of Intelligent and Fuzzy Systems, 36(5), 4065-4075.

  • Sewak, M., & Singh, S. (2016). IoT and distributed machine learning powered optimal state recommender solution. In 2016 International Conference on Internet of Things and Applications (IOTA) (pp. 101-106). IEEE Publishing.

  • Shang, S., Hui, Y., Hui, P., Cuff, P., & Kulkarni, S. (2014). Beyond personalization and anonymity: Towards a group-based recommender system. In Proceedings of the 29th Annual ACM Symposium on Applied Computing (pp. 266-273).

  • Subramaniyaswamy, V., Manogaran, G., Logesh, R., Vijayakumar, V., Chilamkurti, N., Malathi, D., & Senthilselvan, N. (2019). An ontology-driven personalized food recommendation in IoT-based healthcare system. Journal of Supercomputing, 75(6), 3184-3216.

  • Tarus, J. K., Niu, Z., & Mustafa, G. (2017). Knowledge-based recommendation: A review of ontology-based recommender systems for e-learning. Artificial Intelligence Review, 50(1), 21-48.

  • Twardowski, B., & Ryzko, D. (2016). IoT and context-aware mobile recommendations using Multi-Agent Systems. In 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) (Vol. 1, pp. 33-40). IEEE Publishing.

  • Wang, X., Su, L., Zhou, Q., & Wu, L. (2020). Group recommender systems based on members’ preference for trusted social networks. Security and Communication Networks, 2020, Article 1924140.

  • Wu, X. Q., Zhang, L., Tian, S. L., & Wu, L. (2019). Scenario based e-commerce recommendation algorithm based on customer interest in Internet of things environment. Electronic Commerce Research, 1-17.

  • Yan, B., Yu, J., Yang, M., Jiang, H., Wan, Z., & Ni, L. (2019). A novel distributed social Internet of Things service recommendation scheme based on LSH forest. Personal and Ubiquitous Computing, 1-14.

  • Yao, L., Sheng, Q. Z., Ngu, A. H., & Li, X. (2016). Things of interest recommendation by leveraging heterogeneous relations in the Internet of things. ACM Transactions on Internet Technology, 16(2), 1-25.

  • Yao, L., Wang, X., Sheng, Q. Z., Dustdar, S., & Zhang, S. (2019). Recommendations on the Internet of Things: Requirements, challenges, and directions. IEEE Internet Computing, 23(3), 46-54.

  • Yavari, A., Jayaraman, P. P., & Georgakopoulos, D. (2016). Contextualised service delivery in the Internet of things: Parking recommender for smart cities. In 2016 IEEE 3Rd world forum on Internet of things (WF-iot) (pp. 454-459). IEEE Publishing.

  • Yuan, W., Guan, D., Shu, L., & Niu, J. (2013). Mehanizampretraživanja preporučcitelja za sustave sigurnih preporučcitelja u Internetu stvari [Recommender searching mechanism for trust-aware recommender systems in Internet of things]. Automatika, 54(4), 427-437.

  • Zia, K., Muhammad, A., Saini, D. K., & Ferscha, A. (2018). Agent-based model of smart social networking-driven recommendations system for Internet of vehicles. In International Conference on Practical Applications of Agents and Multi-Agent Systems (pp. 275-287). Springer, Cham.

ISSN 0128-7680

e-ISSN 2231-8526

Article ID


Download Full Article PDF

Share this article

Recent Articles