A Context Aware and Adaptive Methodology for Robust Netflix Recommendation framework

Abstract

Individual users need versatile and appropriate online streaming web processes in the online streaming web services context. In real time, online streaming services should be able to suggest suitable items to a user. Collaborative filtering techniques are used in the majority of current approaches to online video service recommendation systems. Such methods are limited in terms of real-time adaptation and involve users' prior knowledge. As a result, this research proposes a real-time recommendation approach with acceptable QoS that can be used in a variety of scalable and complex environments.

The current approach will explore the environment in order to collect data and then use the information to make decisions. We put the proposed method to the test using real-world data. The framework introduces Adaptive Recommendation for online web streaming services, an improved recommendation approach that incorporates online streaming services recommendation by analyzing users' viewing and browsing history. Initially, it groups users together based on their browsing experience and habits. Second, Collaborative Filtering along with association rule mining are used to retrieve each cluster's tastes and behavioral patterns. Finally, it produces a variable-size customized recommendation set.

The suggested scheme employs a shared recommender system that matches users' browsing histories to suggest online streaming items. To uncover patterns between online streaming objects, the proposed method uses data mining techniques.