Social Intimacy Based IoT Services Mining of Massive Data

Zhou, Anni; Feng, Yinan; Xu, Jie

doi:10.1109/icdmw.2017.91

Cited by 8 publications

(6 citation statements)

References 7 publications

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“…In future business and commerce networks of smart cities, filtering and recommending IoT services may represent crucial functionalities. Examples of service recommender systems are found in (Mashal et al, 2016;Zhou et al, 2017;Comi and Rosaci, 2017).…”

Section: Recommender Systems and Smart Economymentioning

confidence: 99%

Recommender systems for smart cities

Quijano-Sánchez

Cantador

Cortés-Cediel

et al. 2020

Information Systems

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Section: Recommender Systems and Smart Economymentioning

confidence: 99%

Recommender systems for smart cities

Quijano-Sánchez

Cantador

Cortés-Cediel

et al. 2020

Information Systems

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“…Ever since then, a considerable amount of work on social learning formulated from many variants of the multi-armed bandit has been done. Much of this work fits best into the stochastic multi-armed bandit framework, where the loss distribution is independent and identically distributed (i.i.d) [19]- [23]. However, this work is experimental and lacks proper theoretical analysis [19]- [21].…”

Section: Related Workmentioning

confidence: 99%

“…Much of this work fits best into the stochastic multi-armed bandit framework, where the loss distribution is independent and identically distributed (i.i.d) [19]- [23]. However, this work is experimental and lacks proper theoretical analysis [19]- [21]. Social learning problems are also addressed under the contextual bandit, where actions are chosen every round after observing some side information [19], [21], [24], [25].…”

Section: Related Workmentioning

confidence: 99%

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Learning the Truth in Social Networks Using Multi-Armed Bandit

Odeyomi

2020

IEEE Access

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This paper explains how agents in a social network can learn the arbitrary time-varying true state of the network. This is practical in social networks where information is released and updated without any coordination. Most existing literature for learning the true state using the non-Bayesian learning approach, assumes that this true state is fixed, which is impractical. To address this problem, the social network is modeled as a graph network, and the time-varying true state is treated as a multi-armed bandit problem. The few works that have applied multi-armed bandit to a social network did not take into consideration the adversarial effects. Therefore, this paper proposes two non-stochastic multi-armed bandit algorithms that can handle the time-varying true state, even in the presence of an oblivious adversary. Regret bounds on the algorithms are obtained, and the simulation performance shows that all agents can converge to the most stable state. The sublinearity of the proposed algorithms is also compared with two well-known non-stochastic multi-armed bandit algorithms.INDEX TERMS strongly connected network, non-Bayesian learning, diffusion learning, multi-armed bandit, regret.

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“…The authors adapted the framework to design a smart parking application. Zhou et al [128] proposed a model which could improve recommendations accuracy by exploiting context-awareness. The model consists of three main components: firstly, the server, which is responsible for providing services recommendations to users; secondly, the user, who provides the context to the server in order to get recommendations; and thirdly, the services provided by the server.…”

Section: Context-aware Recommendationmentioning

confidence: 99%

Recommender Systems for the Internet of Things: A Survey

Altulyan,

Yao,

Wang

et al. 2020

Preprint

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Recommendation represents a vital stage in developing and promoting the benefits of the Internet of Things (IoT). Traditional recommender systems fail to exploit ever-growing, dynamic, and heterogeneous IoT data. This paper presents a comprehensive review of the state-of-the-art recommender systems, as well as related techniques and application in the vibrant field of IoT. We discuss several limitations of applying recommendation systems to IoT and propose a reference framework for comparing existing studies to guide future research and practices.

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Social Intimacy Based IoT Services Mining of Massive Data

Cited by 8 publications

References 7 publications

Recommender systems for smart cities

Recommender systems for smart cities

Learning the Truth in Social Networks Using Multi-Armed Bandit

Recommender Systems for the Internet of Things: A Survey

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