# Variational Inference for a Recommendation System in IoT Networks Based on [PERSON]'s Identity
## 6 Conclusions  
Providing convenient and useful IoT services requires solving the problems facing the online recommendation systems. The target of this paper is to propose and verify a novel Stein variational inference based recommendation algorithm--SVRS that infers the ground truth density of latent variables and predicts the ratings of items they might give. Users who have not viewed a particular piece of content can also be predicted via this  
Figure 4: RMSE and MAE Comparison. (**a**) comparison under MovieLens data; (**b**) comparison under Douban data; (**c**) comparison under MovieLens data; (**d**) comparison under Douban data. algorithm. We offer insights into how user ratings are formed in our SVRS, and we can extend its capabilities to incorporate more dimensions. SVRS performs better than existing algorithms by simulations. In the future, we will try to apply SVRS to more applications to solve the most prominent problems in the industry and continue to study the most accurate and practical ground truth analysis methods. Specifically, there are four promising areas that can be explored. The first is how to identify the factors or parameters that can influence the performances of SVRS. In the current literature, most authors compare their models' best performance with others to verify them. However, any model's performance is not fixed, all subject to the change of settings, data quality, model dimension and model complexity. It is urgent to build a unified platform that can show readers and researchers the advantages and disadvantages of SVRS for their information. The second is how to build SVRS based recommendation models that can adapt to complex network structures. In the existing literature, most Stein variational recommendation-based models use a different simplification method to deal with networks with complex structures. Therefore, it is necessary to develop recommendation models that require less simplification and adapt to complex models. The third is how to unify SVRS with different kinds of recommendation models. As we have seen in the above discussions, SVRS has a close relationship with other recommendation models, such as CF and PMF. There is no doubt that all those methods share some common methodology inherently. However, how to unify them together, or if they have differences that cannot be reconciled, the exact differences between them remain unexplored in the literature. The last is how to apply SVRS in the fields where there is no satisfying solution for recommendation or prediction. Since SVRS is a rather new tool in the literature, many fields lack the application of the Stein Variational-based method to solve the most prominent problems. Therefore, the wide application of the Stein variational recommendation-based method can not only promote the theoretical understanding of itself but also contribute to the research of other fields. Conceptualization, J.L. ; Funding acquisition, Y.C. ; Investigation, J.L. ; Methodology, J.L. and Y.C. ; Project administration, S.M.N.I. ; Supervision, M.A. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Science Foundation of China (Grant No. 61802097), the Natural Science Foundation of Jiangsu Province (No.BK20131277). Not applicable. This data can be found here: [[https://grouplens.org/datasets/movielens/](https://grouplens.org/datasets/movielens/)]([https://grouplens.org/datasets/movielens/](https://grouplens.org/datasets/movielens/)), [[https://www.kesci.com/home/dataset/58](https://www.kesci.com/home/dataset/58) acf61d2445916845b4033]([https://www.kesci.com/home/dataset/58](https://www.kesci.com/home/dataset/58) acf61d2445916845b4033). We note that a shorter conference version of this paper appeared in IECON 2021. The shorter conference paper has been completely revised, improved and re-written. Moreover, our initial conference paper did not address the proof of unbiasedness and variance.