IMPROVING THE ACCURACY OF BUSINESS RECOMMENDATION SYSTEM USING DISTANCE METRIC TECHNIQUES

Computer Science

The recommendation system is facing the problem of how to overcome sparsity, scalability and cold start issues. The existing capsule networks take time in training making it a slow algorithm. Existing literature fills up those sparse values with column or row means. as mean ignores the underlying correlation of data, it sacrifices accuracy. Therefore, to address these issues, this research proposed a hybrid collaborative base recommendation system using an improved SVD and distance metric algorithm to improve accuracy and sparsity issues. Experimental result shows that the proposed model has consistently performed better than all the three state-of-the-art methods including the Capsule Neural Network CF algorithm, the KNN CF algorithm and the SVD+SOM clustering base recommender system. This study has proven that data mining can help companies and business managers to visualize hidden patterns and trends in datasets that were not visible before. Whatever insights are revealed, they make clear decisions that benefit the company, the customers, and the stakeholders they serve

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