Xu, Guangquan, Zhao, Yuyang, Litao, Jiao, Feng, Meiqi, Ji, Zhong, Panaousis, Emmanouil ORCID: 0000-0001-7306-4062, Chen, Si and Zheng, Xi (2020) TTSVD: an efficient sparse decision making model with two-way trust recommendation in the AI enabled IoT systems. IEEE Internet of Things, 8 (12). pp. 9559-9567. ISSN 2327-4662 (Online) (doi:https://doi.org/10.1109/JIOT.2020.3006066)

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Abstract

The convergence of AI and IoT enables data to be quickly explored and turned into vital decisions, and however, there are still some challenging issues to be further addressed. For example, lacking of enough data in AI-based decision making (so called Sparse Decision Making, SDM) will decrease the efficiency
dramatically, or even disable the intelligent IoT networks. Taking the intelligent IoT networks as the network infrastructure, the recommendation systems have been facing such SDM problems. A naive solution is to introduce so-called trust information. However, trust information also maybe face the difficulty of sparse trust evidence (a.k.a sparse trust problem). In our work, an accurate sparse decision making model with two-way trust recommendation in the AI enabled IoT systems is proposed by us, named TT-SVD. Our model incorporates both trust information and rating information more completely, which can efficiently alleviate the above mentioned sparse trust problem and therefore be able to solve the cold start and data sparsity problems. Specifically, we first consider the two-fold trust influences from both trustees and trustors, which can be represented by a factor called trust propensity. To this end, we propose a dual model, including the trustor model (TrustorSVD) and a trustee model (TrusteeSVD) based on an existing rating-only recommendation model called SVD++, which are integrated by the weighted average and yield the final model, TT-SVD. The experimental results show that our model outperforms the state of the art including SVD and TrustSVD in both the ”all users” and ”cold start users” cases, and the accuracy improvement can reach a maximum of 29%. Complexity analysis shows that our model is equally suitable for the case of large sparse datasets. In a word, our model can effectively solve the sparse decision problem by introducing the two-way trust recommendation, and hence improve the efficiency of the intelligent recommendation systems.

Item Type:	Article
Uncontrolled Keywords:	AI enabled IoT systems, collaborative filtering, two-way trust recommendation, intelligent recommendation system, sparse decision making
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > Internet of Things and Security Research Centre (ISEC) Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	23 May 2022 10:24
URI:	http://gala.gre.ac.uk/id/eprint/28670

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