Variational Inference for a Recommendation System in IoT Networks Based on Stein’s Identity

Journal article

Liu, J., Chen, Y., Islam, Sardar M. N. and Alam, M. (2022). Variational Inference for a Recommendation System in IoT Networks Based on Stein’s Identity. Applied Sciences. 12 (4), p. e1816. https://doi.org/10.3390/app12041816
AuthorsLiu, J., Chen, Y., Islam, Sardar M. N. and Alam, M.
AbstractThe recommendation services are critical for IoT since they provide interconnection between various devices and services. In order to make Internet searching convenient and useful, algorithms must be developed that overcome the shortcomings of existing online recommendation systems. Therefore, a novel Stein Variational Recommendation System algorithm (SVRS) is proposed, developed, implemented and tested in this paper in order to address the long-standing recommendation problem. With Stein’s identity, SVRS is able to calculate the feature vectors of users and ratings it has generated, as well as infer the preference for users who have not rated certain items. It has the advantages of low complexity, scalability, as well as providing insights into the formation of ratings. A set of experimental results revealed that SVRS performed better than other types of recommendation methods in root mean square error (RMSE) and mean absolute error (MAE).
Keywordsrecommendation algorithm; Stein variational; variational inference; Internet of Things; Stein’s identity
Year2022
JournalApplied Sciences
Journal citation12 (4), p. e1816
PublisherMDPI
ISSN2076-3417
Digital Object Identifier (DOI)https://doi.org/10.3390/app12041816
Funder/ClientNational Science Foundation of China
Publication dates
Online10 Feb 2022
Publication process dates
Accepted06 Feb 2022
Deposited24 Mar 2022
Publisher's version
License
File Access Level
Open
Licensehttps://creativecommons.org/licenses/by/4.0/
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