Implicit Feedback-based Group Recommender System for Internet of Thing Applications

TL;DR

This paper introduces GREPING, a novel group recommender system for IoT social media that leverages implicit feedback and probabilistic inference to improve recommendation accuracy and robustness.

Contribution

It presents a new implicit feedback-based approach using Bayesian inference and game theory, addressing limitations of explicit feedback reliance in IoT social media recommendations.

Findings

GREPING outperforms baseline methods in efficiency.

GREPING demonstrates high robustness and stability.

Experimental results confirm significant improvements in recommendation quality.

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.