7Personalized Multi-User-Based Movie and Video Recommender System: A Deep Learning Perspective
Jayaramu H. K.1, Suman Kumar Maji2* and Hussein Yahia3
1Indian Institute of Technology (Indian School of Mines), Dhanbad, India
2Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna, Bihar, India
3Centre de recherche INRIA Bordeaux Sud-Ouest, 200 rue de la Vieille Tour, Talence Cedex, France
Abstract
The internet is the prime source to watch movies and micro-videos on platforms like YouTube, Netflix and many popular websites. All these online platforms are query-based search engines which extends a burden to the user to search and find a movie or video of their choice. The problem can be solved by developing better video recommender systems that will assist users in finding more helpful content and improving their overall experience. Deep learning is the leading solution for a large volume of multimedia data for personalized recommendations based on user interests. Feature-based solutions for video recommendation systems can be broadly classified under seven different categories: 1) User Embeddings – determining a user’s specific interests, 2) Representation of Item – the user’s dynamic interest based on their historically accessed items, 3) neighbour-assisted representation – we find similar users history data for generating Neighbour (history) interest information, 4) Categorical representation – It is learned by classifying the user’s historical ...
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