A Novel Recommendation System for Mutual Management on Both OTT Video Streaming and Advertising Using Incremental Learning Approach

Abstract

In today’s trend OTT video service provides a comfortable and luxurious lifestyle and users are easily adapting to it. Advertisements played in middle of the video are the main way of promoting the products or video services. The paper represents a novel approach “Mutual Management over OTT video streaming and Advertisements” (MMOA) based on the incremental learning of user perspective and OTT providers views. The proposed system uses a recommendation system and deep learning to analyze the user and provide service according to their requirements. Here the advertisement influencing is the main target for OTT providers, where the advertisements are chosen to be promoted in exact and minimal time validating the user choice of video streaming. Further, the paper uses machine learning (ML) producing recommendation engines to filter the most viewed video streaming and produces recommendation of ads/videos to user accordingly. Hereby, by the process implementation the OTT platform services and advertisements promotions are improved. An efficient Quality of Experience (QOE) for subscribers in OTT platform is ensured guaranteeing a full-fledged entertainment. An experimental analysis is made from collecting the Kaggle dataset using Github Repository to evaluate the user preference and the effectiveness of the proposed system is produced.