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Performance evaluation of recommendation algorithms on Internet of Things services

Author

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  • Mashal, Ibrahim
  • Alsaryrah, Osama
  • Chung, Tein-Yaw

Abstract

Internet of Things (IoT) is the next wave of industry revolution that will initiate many services, such as personal health care and green energy monitoring, which people may subscribe for their convenience. Recommending IoT services to the users based on objects they own will become very crucial for the success of IoT. In this work, we introduce the concept of service recommender systems in IoT by a formal model. As a first attempt in this direction, we have proposed a hyper-graph model for IoT recommender system in which each hyper-edge connects users, objects, and services. Next, we studied the usefulness of traditional recommendation schemes and their hybrid approaches on IoT service recommendation (IoTSRS) based on existing well known metrics. The preliminary results show that existing approaches perform reasonably well but further extension is required for IoTSRS. Several challenges were discussed to point out the direction of future development in IoTSR.

Suggested Citation

  • Mashal, Ibrahim & Alsaryrah, Osama & Chung, Tein-Yaw, 2016. "Performance evaluation of recommendation algorithms on Internet of Things services," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 646-656.
    • Handle: RePEc:eee:phsmap:v:451:y:2016:i:c:p:646-656
    DOI: 10.1016/j.physa.2016.01.051
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    Cited by:

    1. Yin, Likang & Deng, Yong, 2018. "Measuring transferring similarity via local information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 498(C), pages 102-115.
    2. Mashal, Ibrahim & Alsaryrah, Osama & Chung, Tein-Yaw & Yuan, Fong-Ching, 2020. "A multi-criteria analysis for an internet of things application recommendation system," Technology in Society, Elsevier, vol. 60(C).

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