Whale Optimization Based Recommendation System

Abstract

Recommendation System is an information filtering system which seeks to predict the “liking” of a user for an item, with the aim to suggest the user those items which he/she is most likely to select/buy. The focus of this paper is on rating prediction whose main objective is to predict the ratings the current user is going to give to the items which are yet to be rated/viewed by him/her. This paper uses a collaborative filtering based approach for generating recommendation, and the model used is a clustering-based model. In this approach all the existing users are clustered using whale optimization technique, instead of traditional clustering approaches like k-means, EM algorithm, etc. The appropriate cluster is then identified for the active user, and the ratings of the active user are predicted based on ratings given by other users belonging to the same cluster. Different measures like MAE, SD, RMSE and t-value are used for performance analysis of the proposed method and the results obtained are found to be highly accurate

Hybrid Sparrow Clustered (HSC) Algorithm for Top-N Recommendation System

“…Several techniques have been devised for the development of recommender systems, including swarm-based algorithms [19]. These algorithms are nature-inspired and based on the decision-making capabilities of decentralized and self-organized clans of organisms in a community.…”