Blockchain-based recommender systems: Applications, challenges and future opportunities

Himeur, Yassine; Sayed, Aya; Alsalemi, Abdullah; Bensaali, Fayçal; Amira, Abbes; Varlamis, Iraklis; Eirinaki, Magdalini; Sardianos, Christos; Dimitrakopoulos, George

doi:10.1016/j.cosrev.2021.100439

Cited by 80 publications

(37 citation statements)

References 109 publications

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“…Various recent studies have demonstrated that energy usage data may enable unveiling consumers' private information (e.g., occupancy patterns or economic status). 185 Moreover, complex online NILM systems require advanced cloud servers to run their algorithms; thus, transmitting end-users' data to remote cloud serves is a necessity that can also expose this data to further leakage and intrusion. 167 Typically, existing research frameworks that studied the privacy issues in NILM-based metering have pointed that smart meters can be easily used for: (i) identifying households' employment and occupancy patterns; (ii) enabling burglars to detect vacant properties; (iii) gathering information of end-users' habits and behaviors and use it for illegal activities; and (iv) enabling commercial organizations for using collected data in marketing activities and intrusive direct sales.…”

Section: Security and Privacy Preservationmentioning

confidence: 99%

“…Thus, according to the energy systems experts, this has the potentiality of taking care of the security-related issues in NILM and intelligent metering systems. 185 To that end, blockchain technology can be a reference point as a possible remedy to the emerging security and privacy-preservation issues arising from the implementation of NILM systems. It can not only maintain a good energy disaggregation accuracy of NILM algorithms but also contributes to the reliability and security of NILM systems due to its resilience, adaptability, fault tolerance, and trust characteristics.…”

Section: Improving the Exploitation Of Nilm Systems (E)mentioning

confidence: 99%

“…Explicitly, information gathered or extracted by NILM systems could be served to invade end‐users' privacy. Various recent studies have demonstrated that energy usage data may enable unveiling consumers' private information (e.g., occupancy patterns or economic status) 185 . Moreover, complex online NILM systems require advanced cloud servers to run their algorithms; thus, transmitting end‐users' data to remote cloud serves is a necessity that can also expose this data to further leakage and intrusion 167 .…”

Section: Open Challengesmentioning

confidence: 99%

“…It provides identification security by using private–public encryption along with crucial access. Thus, according to the energy systems experts, this has the potentiality of taking care of the security‐related issues in NILM and intelligent metering systems 185 …”

Section: Future Directionsmentioning

confidence: 99%

See 3 more Smart Citations

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Himeur

Alsalemi

Bensaali

et al. 2022

Int J of Intelligent Sys

Self Cite

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Smart nonintrusive load monitoring (NILM) represents a cost-efficient technology for observing power usage in buildings. It tackles several challenges in transitioning into a more effective, sustainable, and digital energy efficiency environment. This paper presents a comprehensive review of recent trends in the NILM field, in which we propose a multiperspective classification of existing smart NILM techniques. More attention is devoted to describing the contributions of deep learning, feature extraction, computing platforms, and application scenarios for NILM development. Accordingly, NILM technical aspects are first Int J Intell Syst. 2022;37:7124-7179. wileyonlinelibrary.com/journal/int investigated, including data collection devices and public data sets. Next, event-based and non-eventbased NILM algorithms are overviewed. Furthermore, potential limitations of existing solutions are identified, highlighting their technical challenges, especially those related to security and privacy preservation, data scarcity, results reproduction, and implementation and business difficulties. Lastly, future directions are explored to overcome the identified limitations.

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Section: Security and Privacy Preservationmentioning

confidence: 99%

Section: Improving the Exploitation Of Nilm Systems (E)mentioning

confidence: 99%

Section: Open Challengesmentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

See 2 more Smart Citations

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Himeur

Alsalemi

Bensaali

et al. 2022

Int J of Intelligent Sys

Self Cite

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show abstract

“…Recommendation systems may sometimes be biased by only suggesting the most rated or the most liked movies based on a limited assessment of all the possible cluster features. By using the top-N theory to make the recommendation, those that do not meet the threshold of these can find better algorithms that consider the sparsity of information available [107]. Some of the methods such as PCA-SOM map all the features on a lattice; hence the user can find most of the features.…”

Section: Sparsitymentioning

confidence: 99%

Movie Recommender Systems: Concepts, Methods, Challenges, and Future Directions

Jayalakshmi

Ganesh

Čep

et al. 2022

Sensors

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Movie recommender systems are meant to give suggestions to the users based on the features they love the most. A highly performing movie recommendation will suggest movies that match the similarities with the highest degree of performance. This study conducts a systematic literature review on movie recommender systems. It highlights the filtering criteria in the recommender systems, algorithms implemented in movie recommender systems, the performance measurement criteria, the challenges in implementation, and recommendations for future research. Some of the most popular machine learning algorithms used in movie recommender systems such as K-means clustering, principal component analysis, and self-organizing maps with principal component analysis are discussed in detail. Special emphasis is given to research works performed using metaheuristic-based recommendation systems. The research aims to bring to light the advances made in developing the movie recommender systems, and what needs to be performed to reduce the current challenges in implementing the feasible solutions. The article will be helpful to researchers in the broad area of recommender systems as well as practicing data scientists involved in the implementation of such systems.

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Developing smart city services using intent‐aware recommendation systems: A survey

Rafique

Hafid

Qadir

2023

Trans Emerging Tel Tech

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Smart cities could be defined as urban areas that use Information and Communication Technology (ICT) to solve city problems in efficient and sustainable ways. Intent-aware Recommender Systems (IARS) within ICT play a crucial role in filtering useless information according to user demands and assist in decision-making in various smart city platforms. In smart cities, the user traces on IoT, RFIDs, mobiles, and smart sensors capture actual user intent of performing an activity and enhance user satisfaction by proposing optimal services. This paper presents a detailed literature survey of the field of IARS and how it can be used for developing smart city services. First, we present the evolution of IARS with the development of computing technology. Then, we present case studies, synergies, advances, and a reference implementation architecture of IARS for smart cities. We discuss requirements for developing smart city services using IARS. Furthermore, we devise a comprehensive taxonomy of applications and techniques of IARS using different performance parameters. Finally, we elaborate on current issues, challenges, and future research directions in IARS; these directions we believe will pave the way for autonomous service provisioning in smart cities.

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Blockchain-based recommender systems: Applications, challenges and future opportunities

Cited by 80 publications

References 109 publications

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Movie Recommender Systems: Concepts, Methods, Challenges, and Future Directions

Developing smart city services using intent‐aware recommendation systems: A survey

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