A Feedback-corrected Collaborative Filtering for Personalized Real-world Service Recommendation

Shuai Zhao, Yang Zhang, Bo Cheng, Jun-liang Chen

Abstract


The emergence of Internet of Things (IoT) integrates the cyberspace
with the physical space. With the rapid development of IoT, large amounts of IoT
services are provided by various IoT middleware solutions. So, discovery and selecting
the adequate services becomes a time-consuming and challenging task. This paper
proposes a novel similarity-measurement for computing the similarity between services
and introduces a new personalized recommendation approach for real-world service
based on collaborative filtering. In order to evaluate the performance of proposed
recommendation approach, large-scale of experiments are conducted, which involves
the QoS-records of 339 users and 5825 real web-services. The experiments results
indicate that the proposed approach outperforms other compared approaches in terms
of accuracy and stability.


Keywords


Internet of Things, service recommendation, similarity measurement, collaborative filtering

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DOI: https://doi.org/10.15837/ijccc.2014.3.1085



Copyright (c) 2017 Shuai Zhao, Yang Zhang, Bo Cheng, Jun-liang Chen

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