# Variational Inference for a Recommendation System in IoT Networks Based on [PERSON]'s Identity
###### Abstract
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification** & **Disadvantages** & **Advantages** \\ \hline \multirow{4}{*}{Collaborative filtering-based} & High computing cost when & \\  & dealing with large data, low & Direct and intuitive \\  & accuracy in the existence of & to implement \\  & sparsity, difficulty to explain & \\ \hline \multirow{2}{*}{Social network-based} & Unstable accuracy, lacks & Works well for most \\  & individuality and freshness & social networks \\ \hline \multirow{2}{*}{Content-based} & \multirow{2}{*}{Cold start and overfitting} & Works well for field require \\  & & professional knowledge \\ \hline \multirow{4}{*}{Deep learning-based} & Require big data, only suitable & \\  & for situations involving visual & Powerful computing ability, \\  & elements and only keyword & often more accurate \\  & match is not sufficient & \\ \hline \multirow{4}{*}{Variational inference-based} & Accuracy or complexity & \\  & depend on the complexity of & Reduce overfitting, efficient in \\  & assumptions of target density, & computing hyper-parameters \\ \cline{1-1}  & too specific & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Advantages and disadvantages of different recommendation methods. narios that contain visual elements and for which only word searching is not sufficient, such as garment recommendation. Thus, we need other recommendation algorithms to complement the DL-based method. The VI-based approach has become popular among researchers because it helps reduce overfitting and efficiently solves hyperparameters. A variational Bayesian matrix factorizations-based movie recommendation algorithm developed by [PERSON] et al. in 2013 is one example. Using variational Bayesian methods, [PERSON] et al. developed a method for predicting TV program feedback. A 2015 paper by [PERSON] and his colleagues [22] described an approach that utilizes autoencoders (a VI approach) with CF-based recommendation systems. In a study by [PERSON] et al. on deep optimization of sparse data using variational matrix factorization, they propose an algorithm for making recommendations based on a large scale sparse rating matrix [23]. Then, in 2019, [PERSON] et al. [24] developed a recommendation system that uses both deep learning, VI, and matrix factorization, called deep variational matrix factorization (DVMF). However, the accuracy and complexity of VI depend in large part on how complex the target density is, and without full knowledge, it is difficult to construct general VI models. Thus, to use a VI model in different scenarios, we have to construct VI algorithms that improve generality [25]. Recently, [PERSON]'s method, another class of VI method, is a general tool to obtain bounds on distances between distributions, offering a powerful density approximation solution [26]. In this paper, inspired by the generalization and low-complexity of [PERSON]'s method, we offer a Stein Variational Recommendation System that reduces overfitting, incorporates large volumes of data, and has lower MAE and RMSE. In this paper, we develop and analyze an SVRS algorithm both theoretically and experimentally in terms of accuracy and complexity. We also provide justifications and characteristics of our SVRS in the appendix. We discuss the concept of SVRS in Section 2, and we provide a detailed explanation of SVRS in Section 3. In Section 4, we simulate experimental comparisons of SVRS with others. In the end, the paper is concluded in Section 6.