Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Application of Liquid Rank Reputation System for Content Recommendation. / Saxena, Abhishek; Kolonin, Anton.
Proceedings - 2022 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2022. Institute of Electrical and Electronics Engineers Inc., 2022. стр. 196-199 (Proceedings - 2022 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2022).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
TY - GEN
T1 - Application of Liquid Rank Reputation System for Content Recommendation
AU - Saxena, Abhishek
AU - Kolonin, Anton
N1 - Publisher Copyright: © 2022 IEEE.
PY - 2022/11/3
Y1 - 2022/11/3
N2 - An effective content recommendation on social media platforms should be able to benefit both creators to earn fair compensation and consumers to enjoy really relevant, interesting, and personalized content. In this paper, we propose a model to implement the liquid democracy principle for the content recommendation system. It uses a personalized content recommendation model based on reputation ranking system to encourage personal interests driven recommendation. Moreover, the personalization factors to an end users' higher-order friends on the social network (initial input Twitter channels in our case study) to improve the accuracy and diversity of recommendation results. This paper analyzes the dataset based on cryptocurrency news on Twitter to find the opinion leader using the liquid rank reputation system. This paper deals with the tier-2 implementation of a liquid rank in a content recommendation model. This model can be also used as an additional layer in the other recommendation systems. The paper proposes the implementation, challenges, and future scope of the liquid rank reputation model.
AB - An effective content recommendation on social media platforms should be able to benefit both creators to earn fair compensation and consumers to enjoy really relevant, interesting, and personalized content. In this paper, we propose a model to implement the liquid democracy principle for the content recommendation system. It uses a personalized content recommendation model based on reputation ranking system to encourage personal interests driven recommendation. Moreover, the personalization factors to an end users' higher-order friends on the social network (initial input Twitter channels in our case study) to improve the accuracy and diversity of recommendation results. This paper analyzes the dataset based on cryptocurrency news on Twitter to find the opinion leader using the liquid rank reputation system. This paper deals with the tier-2 implementation of a liquid rank in a content recommendation model. This model can be also used as an additional layer in the other recommendation systems. The paper proposes the implementation, challenges, and future scope of the liquid rank reputation model.
KW - liquid democracy
KW - peer-to-peer systems
KW - recommendation systems
KW - reputation system
KW - social computing
UR - http://www.scopus.com/inward/record.url?scp=85141845691&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/4c8eae57-2b38-35d9-af6f-ce19018e62cf/
U2 - 10.1109/USBEREIT56278.2022.9923352
DO - 10.1109/USBEREIT56278.2022.9923352
M3 - Conference contribution
AN - SCOPUS:85141845691
T3 - Proceedings - 2022 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2022
SP - 196
EP - 199
BT - Proceedings - 2022 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2022
Y2 - 19 September 2022 through 21 September 2022
ER -
ID: 39471981