Mining Competitors and Finding Winning Plans Using Feature Scoring and Ranking-Based CMiner++ Algorithm: Finding Top-K Competitors

For a business to succeed, it is very important to make things speaking more to clients than to rivals. It is more critical to decide on the significant feature of an item which influences its competency. In spite of the works that have been done already, a few algorithms gained efficient solution. This paper proposes the CMiner++ Algorithm to assess the competitive relationship among items in unstructured dataset and finding the Top-K competitors of a given item. Definitively, the nature of the outcomes and the versatility of this methodology utilizing numerous datasets from various areas are assessed, and the efficiency and adaptability of this algorithm on various data sets are improved when compared to existing algorithms. In today's busy world, automatic recommendation systems are emerging because people are looking for the products best suited for them. So, it is very important to analyse the behaviour of people, make a review on large and large unstructured data sets, and make the fully automated deep learning system to ensure the accurate outcome.

Automated summary

To ensure business success, it is crucial to prioritize clients over rivals and identify the key feature that influences a product's competitiveness. Despite previous efforts, only a few algorithms have yielded efficient solutions. This paper introduces the CMiner++ Algorithm to evaluate competitive relationships among items in unstructured datasets and identify the top competitors. The algorithm demonstrates improved efficiency and adaptability compared to existing methods, making it vital in today's world where automated recommendation systems are increasingly sought after.