Contextualized Movie Recommendation Framework to Improve User Experience for OTT

Objectives: The purpose of the present investigation is to build a contextual recommendation framework based on user history and proven machine learning algorithms to improve user experience. New OTT entrants can adopt the findings to create their recommendation system or monetize it for other providers. Methods: It follows a hybrid approach where standard, proven algorithms used by large OTT providers and other researchers are evaluated using open-source technologies on the MovieLens-1M dataset. The proposed model describes additional factors like user feedback, contextual search and key performance indicators to evaluate the recommendation framework for improved user experience. Findings: As per the findings, a hybrid framework of Content-Based Filtering, Collaborative Filtering (CF), SVD++ and RBM can provide 80% of the tray requirements of modern OTT platforms. By using open-source libraries and various optimization techniques not only we can achieve a lower error rate, but we can improve user experience by considering other factors like novelty, diversity etc. Novelty and application: The manuscript provides the minimal set of algorithms that can provide the maximum tray requirement of any OTT platform, along with key performance indicators. It also describes contextual time-based tags generated from a sample video, which can be searched by the user in near real time. The proposed service-based framework once implemented, can also be passed to other providers for monetization. Keywords: Recommendation, OTT, Contextual Search, Content Based Filtering, CF, SVD