Design of Recommender System using Content Based Filtering and Collaborative Filtering Technique: A Comparative Study

Abstract

 Recommendation based systems have gained a lot of popularity due to their wide range of applicability. From e-commerce-based product recommendation, to social media-based friend recommendation, these systems can be used for any kind of pattern analysis targeted to recommending data based on interlinked usage statistics. To refine the aspect of such systems, they must have a strong pattern recognition engine, combined with a strong prediction engine. Because, a strong pattern recognition engine will be able to analyze and distinguish different patterns effectively, and the prediction engine will be able to merge these patterns together in order to predict the recommendation for the system. Generally, algorithms like neural network, k-means, kNN and SVM based pattern analyzers are combined with neighborhood-based, context-aware pattern analysis-based and collaborative filtering-based predictors in order to develop a complete recommendation system. Many authors have also combined recommender systems in order to generate a high-performance hybrid recommender. In this paper we have studied different recommender systems and observed their statistical performance under various recommendation conditions; present the design of recommender system using two cutting edge method content based and collaborative filtering. These systems are then ranked as per their performance parameters in order to recommend the best recommender system for a given application under test. Furthermore, recommendations are so as to additionally enhance efficacy of these systems.

 Keywords: Recommender, kNN, neighborhood, pattern, prediction, collaborative, context, content.