A Deterministic Model for Determining Degree of Friendship Based on Mutual Likings and Recommendations on OTT Platforms. (30th June 2022)

• Record Type: Journal Article
• Authors: Khalique, Aqeel; Rahmani, Mohammad Khalid Imam; Saquib, Mohd; Hussain, Imran; Muzaffar, Abdul Wahab; Ahad, Mohd. Abdul; Nafis, Md Tabrez; Ahmad, Mohd Wazih
• Other Names: Bhardwaj Arpit Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-06-30
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/9576468
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : In recent years, the application of various recommendation algorithms on over-the-top (OTT) platforms such as Amazon Prime and Netflix has been explored, but the existing recommendation systems are less effective because either they fail to take an advantage of exploiting the inherent user relationship or they are not capable of precisely defining the user relationship. On such platforms, users generally express their preferences for movies and TV shows and also give ratings to them. For a recommendation system to be effective, it is important to establish an accurate and precise relationship between the users. Hence, there is a scope of research for effective recommendation systems that can define a relationship between users and then use the relationship to enhance the user experiences. In this research article, we have presented a hybrid recommendation system that determines the degree of friendship among the viewers based on mutual liking and recommendations on OTT platforms. The proposed enhanced model is an effective recommendation model for determining the degree of friendship among viewers with improved user experience.