Study Analysis and Designing Hybrid Approach for Recommendation System

Abstract

The recommendation system uses the information related to user interest, preferences, and need to recommend the users with products or services that they are interested in. Various implicit methods exist that finds the user s inclination and automatically suggest the anticipated items in the interface. The problem with such approaches is they do not provide user-oriented results. newlineTherefore, a user-oriented product recommendation system is developed in this research using the proposed Tunicate Swarm Magnetic Optimization Algorithm-based Black Hole renyi Entropy Fuzzy Clustering with K-Nearest Neighbor (TSMOA-based BHrEFC+KNN) to generate more user convenient result by grouping relevant products and recommends the similar products to users with great interest. newline The proposed TSMOA algorithm is developed by integrating the Tunicate Swarm Algorithm (TSA) and Magnetic Optimization Algorithm (MOA), respectively. With the entropy measure and Jaro-Winkler distance, the process of group matching and the matching sequence of visitor and query is performed more effectively that enable us to achieve the sentiment classification based on the binary visitor sequence. The performance obtained by the proposed TSMOA-based BHrEFC+KNN is evaluated in terms of accuracy, True Positive Rate (TPR), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), respectively. newline newline