Variational Inference for a Recommendation System in IoT Networks Based on Stein’s Identity
Source abstract
Abstract
The 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).
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Recommender Systems and Techniques · Stochastic Gradient Optimization Techniques · Caching and Content Delivery
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Cyberspace Security, Hangzhou Dianzi University, Hangzhou 310018, China
Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne 14428, Australia
School of Engineering, London South Bank University, London SE1 0AA, UK
Funding
National Science Foundation of China
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