Research and Application of Film and Television Literature Recommendation Based on Secure Internet of Things and Machine Learning

Jieqiong Zhou,Fangchun Chi,Bin Peng,Zhenhua Wei,Sang-Bing Tsai

doi:10.1155/2021/4066267

Jieqiong Zhou, Fangchun Chi + Show 3 more

Open Access

https://doi.org/10.1155/2021/4066267

Copy DOI

Abstract

Film and television literature recommendation is an AI algorithm that recommends related content according to user preferences and records. The wide application in various APPs and websites provides users with great convenience. This article aims to study the Internet of Things and machine learning technology, combining deep learning, reinforcement learning, and recommendation algorithms, to achieve accurate recommendation of film and television literature. This paper proposes to use the ConvMF-KNN recommendation model to verify and analyze the four models of PMF, ConvM, ConvMF-word2vec, and ConvMF-KNN, respectively, on public datasets. Using the path information between vertices in bipartite graph and considering the degree of vertices, the similarity between items is calculated, and the neighbor item set of items is obtained. The experimental results show that the ConvMF-KNN model combined with the KNN idea effectively improves the recommendation accuracy. Compared with the accuracy of the PMF model on the MovieLens 100 k, MovieLens 1 M, and AIV datasets, the accuracy of the ConvMF model on the above three datasets is 5.26%, 6.31%, and 26.71%, respectively, an increase of 2.26%, 1.22%, and 7.96%. This model is of great significance.

Highlights

With the rapid growth of online film and television literature resources, how to quickly obtain effective information and the latest information from massive amounts of data and information, and how to improve the accuracy and quality of information retrieval to meet the needs of different users for information push, has become an urgent problem to be solved. e diversified information demand is the key to film and television literature recommendation
With the continuous popularization of film and television software, the amount of information in film and television literature will maintain a rapid growth momentum for a period of time in the future, and the negative impact of information overload will become more serious. erefore, the research and application of film and television literature recommendation systems and recommendation algorithms based on the Internet of ings and machine learning have far-reaching significance in resource sharing and solving information overload
Canedo and Skjellum used machine learning in IoT gateways to help protect the system, and they studied the use of artificial neural networks (ANNs) in the gateways to detect anomalies in data sent from edge devices. is method can improve the security of the Internet of ings system [2]

Summary

Introduction

With the rapid growth of online film and television literature resources, how to quickly obtain effective information and the latest information from massive amounts of data and information, and how to improve the accuracy and quality of information retrieval to meet the needs of different users for information push, has become an urgent problem to be solved. e diversified information demand is the key to film and television literature recommendation. Erefore, the research and application of film and television literature recommendation systems and recommendation algorithms based on the Internet of ings and machine learning have far-reaching significance in resource sharing and solving information overload. Compared with the research and application of film and television literature recommendation based on the Internet of ings (IoT) and machine learning, there are many researches on recommendation systems and recommendation algorithms in e-commerce. E classification algorithm includes neural network (NN) and support vector machine (SVM), which can be used to detect DoS attacks at the media access control layer To this end, Canedo and Skjellum used machine learning in IoT gateways to help protect the system, and they studied the use of artificial neural networks (ANNs) in the gateways to detect anomalies in data sent from edge devices. Aiming at the influence of noise samples in support vector, this paper proposes a method to deal with noise by using vector criterion in each cluster

Technology Related to Machine Learning and IoT Security

Experimental Design

Experimental Results and Analysis