TY - GEN
T1 - Implicit Feedback-based Group Recommender System for Internet of Things Applications
AU - Guo, Zhiwei
AU - Yu, Keping
AU - Guo, Tan
AU - Bashir, Ali Kashif
AU - Imran, Muhammad
AU - Guizani, Mohsen
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by the Chongqing Natural Science Foundation of China under grant cstc2019jcyj-msxmX0747, State Language Commission Research Program of China under grant YB135-121, National Natural Science Foundation of China under grant 61901067, and Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research (KAKENHI) under Grant JP18K18044.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - With the prevalence of Internet of Things (IoT)-based social media applications, the distance among people has been greatly shortened. As a result, recommender systems in IoT-based social media need to be developed oriented to groups of users rather than individual users. However, existing methods were highly dependent on explicit preference feedbacks, ignoring scenarios of implicit feedbacks. To remedy such gap, this paper proposes an implicit feedback-based group recommender system using probabilistic inference and non-cooperative game (GREPING) for IoT-based social media. Particularly, unknown process variables can be estimated from observable implicit feedbacks via Bayesian posterior probability inference. In addition, the globally optimal recommendation results can be calculated with the aid of non-cooperative game. Two groups of experiments are conducted to assess the GREPING from two aspects: efficiency and robustness. Experimental results show obvious promotion and considerable stability of the GREPING compared to baseline methods.
AB - With the prevalence of Internet of Things (IoT)-based social media applications, the distance among people has been greatly shortened. As a result, recommender systems in IoT-based social media need to be developed oriented to groups of users rather than individual users. However, existing methods were highly dependent on explicit preference feedbacks, ignoring scenarios of implicit feedbacks. To remedy such gap, this paper proposes an implicit feedback-based group recommender system using probabilistic inference and non-cooperative game (GREPING) for IoT-based social media. Particularly, unknown process variables can be estimated from observable implicit feedbacks via Bayesian posterior probability inference. In addition, the globally optimal recommendation results can be calculated with the aid of non-cooperative game. Two groups of experiments are conducted to assess the GREPING from two aspects: efficiency and robustness. Experimental results show obvious promotion and considerable stability of the GREPING compared to baseline methods.
KW - Internet of Things
KW - group recommender systems
KW - implicit feedback
KW - probabilistic inference
UR - http://www.scopus.com/inward/record.url?scp=85101230792&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85101230792&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM42002.2020.9348091
DO - 10.1109/GLOBECOM42002.2020.9348091
M3 - Conference contribution
AN - SCOPUS:85101230792
T3 - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
BT - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Global Communications Conference, GLOBECOM 2020
Y2 - 7 December 2020 through 11 December 2020
ER -