Advanced Graph Analytics Algorithms On Genre Based Recommending System

It is crucial for online service providers like Netflix to offer their viewers a selection of top-notch movies. In a bipartite network, this kind of challenge can be thought of as a ranking problem. This is a well-known issue that a ranking system can resolve. However, many traditional rating systems have the same flaw they favor older films over recent ones, despite of Basic services the fact that some recent films might have better quality. In the study, we propose a ranking method applying Equality rebalance methodology to minimize the time bias of the ranking in bipartite graphs Environment. Then, using three actual datasets as our ground truth benchmark, we run experiments. The research findings demonstrate that our proposed methodology enhances predictive performance or accuracy by at least 20% and as much as 80% while also lowering the time bias of ranking scores. The contribution of this paper shows the different types of algorithms for recommend the genre type with less computational time.