Performance Evaluation of Ensemble based Collaborative Filtering Recommender System

Recommender Systems are used for finding the needle in the haystack. Recommender systems are of one the innovations in this revolution, which addresses the problem of filtering the data/information that is more likely to. Collaborative filtering is a technique which makes automatic predictions, by collecting the preferences for the users. But collecting the preferences from the user is no easy task. The Collaborative Filtering can also be a user based k-NN collaborative filtering algorithm or an item based k-NN collaborative filtering algorithm. Each type has its merits and demerits. Aiming at this concern, an ensemble based k-NN collaborative filtering is proposed. This work fuses the merits of the user based k-NN algorithm and item-based algorithm. The experiment analysis on the MovieLens dataset gives a consistent model that is accurate and results in better personalized movie recommendations than other conventional models. The experimental results show that ensemble based k-NN algorithm clearly improves enhances the recommender system's performance, and give better recommendation quality.