In the Internet of Things (IoT) environment, predicting the Quality of Service (QoS) is a challenging task due to the diversity of devices and service types. The dispersed nature of data collected from edge devices further complicates data sharing, thereby affecting the integrity and standardization of QoS evaluation. To address these issues, this paper proposes a QoS prediction model named HRMI, which efficiently integrates decentralized data and accommodates heterogeneous information to improve user experience. The HRMI framework comprises three main phases and one information-theoretic loss function: (1)Reputation Assessment, which leverages Utility Maximization theory and a Reputation Hybridization module to derive user and service reputations; (2)Feature Extraction, which employs deep learning techniques to construct user and service feature vectors from contextual attributes such as IDs, regions, and reputations; (3)Feature Interaction, which applies an attention mechanism to capture multi-scale feature dependencies and uncover deeper feature correlations; and (4)Mutual Information Loss, which exploits complementary information among feature vectors by maximizing the mutual information between feature representations and predicted values. Extensive experiments conducted on a widely used real-world dataset demonstrate that HRMI consistently outperforms state-of-the-art baseline methods.