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.
翻译:随着基于Times(IoT)的社交媒体互联网应用的普及,人们之间的距离大大缩短,因此,基于IoT的社交媒体中的建议系统需要面向用户群体而不是个人用户,但是,现有方法高度依赖于明确的偏好反馈,忽视了隐含反馈的假想。为了弥补这种差距,本文件建议采用基于IoT的社交媒体的概率推论和不合作游戏(GREPING)来隐含基于反馈的小组建议系统。特别是,通过Bayesian 远地点概率推断的可见的隐含反馈可以估计出未知过程变量。此外,在不合作游戏的帮助下,可以计算出全球最佳建议结果。进行了两组实验,从两个方面评估GREPING:效率和稳健性。实验结果显示,与基线方法相比,GREPING明显促进并相当稳定。