- Title
- Implicit feedback-based group recommender system for internet of things applications
- Creator
- Guo, Zhiwei; Yu, Keping; Guo, Tan; Bashir, Ali; Imran, Muhammad; Guizani, Mohsen
- Date
- 2020
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/182709
- Identifier
- vital:16165
- Identifier
-
https://doi.org/10.1109/GLOBECOM42002.2020.9348091
- Identifier
- ISBN:9781728182988 (ISBN)
- Abstract
- 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. © 2020 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 2020 IEEE Global Communications Conference, GLOBECOM 2020, Virtual Taipei, 7-11 December 2020 Vol. 2020-January
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright @ 2020 IEEE
- Rights
- Open Access
- Subject
- Group recommender systems; Implicit feedback; Internet of Things; Probabilistic inference
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