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Applications of Deep Learning-Based Product Recommendation Systems
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Author(s): Sunil Sharma (National Institute of Technology, Kurukshetra, India)and Minakshi Sharma (National Institute of Technology, Kurukshetra, India)
Copyright: 2023
Pages: 16
Source title:
Perspectives on Social Welfare Applications’ Optimization and Enhanced Computer Applications
Source Author(s)/Editor(s): Ponnusamy Sivaram (G.H. Raisoni College of Engineering, Nagpur, India), S. Senthilkumar (University College of Engineering, BIT Campus, Anna University, Tiruchirappalli, India), Lipika Gupta (Department of Electronics and Communication Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, India)and Nelligere S. Lokesh (Department of CSE-AIML, AMC Engineering College, Bengaluru, India)
DOI: 10.4018/978-1-6684-8306-0.ch006
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Abstract
The high-tech world we live in today is dominated by multimedia. Multimedia is being created at a rapid rate in the current technological era. Consumption and the exchange of the same between users happen quickly. Choosing whatever form of content or multimedia to consume next depending on interests and preferences is a conundrum while consuming this content. Nowadays, all online streaming sites utilize multimedia recommender systems. These are utilized to anticipate the following collection of multimedia that users can enjoy based on their prior usage patterns. By identifying the points of commonality between the user and the goods, preexisting models can forecast this utilizing the collaborative field. By treating this as a sequence prediction problem, the proposed model in this chapter increases the predicted accuracy using collaborative filtering (CF), ripple nets, deep learning, and recurrent neural networks (RNNs).
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