Trust information network in social Internet of things using trust-aware recommender systems. (April 2020)

• Record Type: Journal Article
• Authors: Son, Juyeon; Choi, Wonyoung; Choi, Sang-Min
• Is Part Of: International journal of distributed sensor networks. Volume 16:Number 4(2020)
• Publication Date: 2020-04
• Subjects: Internet of things -- recommender systems -- social network -- trust-aware -- implicit trust -- trust asymmetry -- trust propagation; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147720908773
• Languages: English
• ISSNs: 1550-1329

Abstract

Social Internet of things is one of the most up-to-date research issues in the applications of Internet of things technologies. In social Internet of things, accuracy and reliability are standard features to discerning decisions. We assume that decision support systems based on social Internet of things could leverage research from recommender systems to achieve more stable performance. Therefore, we propose a trust-aware recommender systems suitable for social Internet of things. Trust-aware recommender systems adapt the concept of social networking service and utilize social interaction information. Trust information not only improves recommender systems from opinion spam problems but also more accurately predicts users' preferences. We confirm that the performance of a recommender system becomes more improved when implicit trust is able to satisfy the properties of trust in the social Internet of things environment. The structure and amount of social link information are context-sensitive, so applying the concept of trust into social Internet of things environments requires a method to optimize implicit and explicit trust with minimal social link information. Our proposed method configures an asymmetric implicit trust network utilizing user–item rating matrix and transforms trust propagation metrics for a directional and weighted trust network. Through experiments, we confirm that the proposed methods enable higher accuracy and wider coverage compared to the existing recommendation methods.