Personalized Multi‐User‐Based Movie and Video Recommender System
H. K. Jayaramu, Suman Kumar Maji, Hussein M. Yahia · 2024
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 items into distinct categories and recognizing their differences, 5) Collaborative representation, 6) Hybrid representation – While neighbour-assisted characterization, which defines user profile from a collaborative perspective, characterises user interest from a customised perspective at the item and category level, and 7) Using rich contents (e.g., scene, meta, motion etc.) – uses to overcome restrictions caused by the absence of specific ones. In this book chapter, we will focus in detail on the principles and deep learning solutions that exist for online video recommendations to users. We will cover in detail the overview and the literature and shall also include an experimental analysis section wherein we shall analyze the performance of the various video recommender systems on different multimedia datasets.