Design and Development of an Efficient Classification Model to Suggest Videos based on Artificial Intelligence Association | IEEE Conference Publication | IEEE Xplore

Design and Development of an Efficient Classification Model to Suggest Videos based on Artificial Intelligence Association


Abstract:

An excellent strategy to increase user pleasure and stickiness is to implement a video recommendation system. This system will give users with acceptable videos to pick f...Show More

Abstract:

An excellent strategy to increase user pleasure and stickiness is to implement a video recommendation system. This system will give users with acceptable videos to pick from. This is why academics and video-sharing websites are so interested in it. Video recommendation systems now employ merged machine learning techniques. Some research has shown that combining Support Vector Machine (SVM) with a content-based recommendation algorithm or a Deep Neural Network with a collaborative filtering method can significantly increase the accuracy of these algorithms. Advances in Deep Learning have been substantial, keeping pace with the burgeoning field of Machine Learning. Since the random sample problem with training efficiency has been resolved, the dependability of multi-layer neural networks has been more apparent, which has piqued the attention of academics in neural network research. Our article proposed AIDCL, an AI-based Deep Classification Logic method, a new way to provide video recommendations. This allows us to compare the suggested scheme's performance in areas such as function approximation and feature extraction to that of the standard deep learning model, Deep Neural Network (DNN). Our goal is to find out how well the proposed scheme works. It would appear that the suggested method, when used to provide video recommendations, will lead to improved accuracy. In this study, we provide a video recommendation engine that demonstrates the efficacy of our algorithm through experiments by integrating a screening algorithm with artificial intelligence logic.
Date of Conference: 23-24 November 2023
Date Added to IEEE Xplore: 31 January 2024
ISBN Information:
Conference Location: CHENNAI, India

I. INTRODUCTION

With the proliferation of mobile devices and advancements in information technology, the e-commerce business is booming throughout the world, and new items of all kinds are being introduced every day [1]. Therefore, individuals experience inconvenience due to the time it takes to navigate the entire website in order to get the goods they want. A major contributor to user unhappiness is the problem of information overload. Hence, a tailored recommendation system is required. Salespeople in this field have long suggested products to clients in an effort to up-sell and cross-sell other products; this has shown to be an effective method for increasing revenue. An approach that attempts to anticipate user preferences and then makes recommendations based on those predictions is called a recommendation system. In order to improve customer satisfaction, recommendation systems strive to provide users with appropriate recommendations and increase their stay on the site.

References

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