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 feedback. 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.