Stein Variational Recommendation System with Knowledge Embedding Enabling the IoT Services

The Internet of Things (IoT) interconnects various devices and services, of which recommendation services are an important component to help the development of IoT applications. Furthermore, without the aid of suitable online recommendation systems, Internet users will be overwhelmed by the tremendous amount of contents. Researchers have thus developed a large volume of recommendations. However, they are all flawed with high complexity, cold start issues, inability to generalize, etc. In recent years, some researchers had turned to variational inference (VI)-based recommendation systems, which can solve the above problems to some extent. However, these VI-based recommendations are merely hybrid methods of VI with the existing recommendation algorithms and are unable to be implemented well in real practices. Therefore, developing algorithms that can overcome these limitations of the existing online recommendation systems is essential for convenient and useful Internet searches. In this paper, we propose, develop, implement and test a more general, new and innovative Stein Variational Recommendation System algorithm (SVRS) to tackle the long plaguing recommendation problems. Based on Stein's identity, the SVRS algorithm can compute the feature vector of existing users and items it had rated, and further predict the ratings for users that have not been engaged with certain content. SVRS provides more general insights into the forming of user ratings, can be easily extended to higher dimensions and has the merits of low complexity, easy scaling and generalizability. Experiments show that SVRS outperforms the other existing type of recommendation algorithms and it has higher accuracy in terms of mean absolute error (MAE) and root mean square error (RM SE).