A survey on blockchain-based Recommender Systems: Integration architecture and taxonomy

Mekouar, Loubna; Iraqi, Youssef; Damaj, Issam; Naous, Tarek

doi:10.1016/j.comcom.2022.01.020

Cited by 14 publications

(2 citation statements)

References 58 publications

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“…A demand-side personalized recommendation system using a non-intrusive appliance load monitoring technique was discussed in [31] to save the appliances' energy in the residencies connected to the smart grid. A thorough survey was conducted on the features and challenges of recommendation systems while integrating them with blockchain technology [32]. All the abovementioned recommendation systems were focused on the likes of users and the recommendations functioned accordingly.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network-Based Personalized Program Recommendation System for Smart Television Users

et al. 2023

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The smart home culture is rapidly increasing across the globe and driving smart home users toward utilizing smart appliances. Smart television (TV) is one such appliance that is embedded with smart technology. The users of smart TV have their interests in the programs. However, automatic recommendation of programs for user-to-user is still under-researched. Several papers discussed recommendation systems, but those are related to different applications. Even though there are some works on recommending programs to smart TV users (single-user and multi-user), they did not discuss the smart TV camera module to capture and validate the user image for recommending personalized programs. Hence, this paper proposes a convolutional neural network (CNN)-based personalized program recommendation system for smart TV users. To implement this proposed approach, the CNN algorithm is trained on the datasets ‘CelebFaces Attribute Dataset’ and ‘Labeled Faces in the Wild-People’ for feature extraction and to detect a human face. The trained CNN model is applied to the user image captured by using the smart TV camera module. Further, the captured image is matched with the user image in the ‘synthetic dataset’. Based on this matching, the hybrid filtering technique is proposed and applied; thereby the recommendation of the respective program is done. The proposed CNN algorithm has achieved approximately 95% training performance. Besides, the performance of hybrid filtering is approximately 85% from the single-user perspective and approximately 81% from the multi-user perspective. From this, it is observed that hybrid filtering outperformed conventional content-based filtering and collaborative filtering techniques.

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Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network-Based Personalized Program Recommendation System for Smart Television Users

et al. 2023

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“…A context-based filter is based on some of the user information derived from the device such as location, and the operating system, and correlates them with what other users from similar contexts are using. In hybrid filtering, the content, context, and user characteristics are used, therefore, if the recommender system does not have any information about the user, it will use the content and context to make the first recommendations [99]. The subsequent recommendations will depend on the hints of information available.…”

Section: Cold Startmentioning

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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Verifiable External Blockchain Calls: Towards Removing Oracle Input Intermediaries

Ellul

Pace

2023

Lecture Notes in Computer Science

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A survey on blockchain-based Recommender Systems: Integration architecture and taxonomy

Cited by 14 publications

References 58 publications

Convolutional Neural Network-Based Personalized Program Recommendation System for Smart Television Users

Convolutional Neural Network-Based Personalized Program Recommendation System for Smart Television Users

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

Verifiable External Blockchain Calls: Towards Removing Oracle Input Intermediaries

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