An Auction-Based Recommender System for Over-The-Top Platform

Abstract

In this era of digital domination, it is fit to say that individuals are more inclined towards viewership on online platforms due to the wide variety and the scope of individual preferences it provides. In the past few years, there has been a massive growth in the popularity of Over-The-Top platforms, with an increasing number of consumers adapting to them. The Covid-19 pandemic has also caused the proliferation of these services as people are restricted to their homes. Consumers are often in a dilemma about which subscription plan to choose, and this is where a recommendation system makes their task easy. The Subscription recommendation system allows potential users to pick the most suitable and convenient plan for their daily consumption from diverse OTT platforms. The economic equilibrium behind allocating these resources follows a unique voting and bidding system propped by us in this paper. The system is dependent on two types of individuals, type 1 seeking the recommendation plan, and type 2 suggesting it. In our study, the system collaborates with the latter who participate in voting and invest/bid in the available options, keeping in mind the user preferences. This architecture runs on an interface where the candidates can login to participate at their convenience. As a result, selective participants are awarded monetary gains considering the rules of the suggested mechanism, and the most voted subscription plan gets recommended to the user. © 2022 Tech Science Press. All rights reserved.