MF-RISE : Benchmarking for Multifaceted Recommender System Engine

Abstract

The advent of the information age has led tothe availability of overwhelming choices of products to userswhich create need of various Recommendation Systems (RS). Recommendation System belongs to the methods of InformationRetrieval, Data Mining and Machine Learning algorithms. Multifaceted Recommendation System Engine (MF-RISE) helpthe users to get personalized recommendations, helps users to select correct product from wide range of products using userfeedback, ratings and reviews provided by users. In real-world scenarios, recommenders have many non-functionalrequirements of technical nature and must handle huge amountdata. Evaluation of Multifaceted Recommendation System Enginemust take these issues into account in order to be producemaximum useful recommendations. The many researchers have proposed a wide range of recommendationsystems algorithms. This study investigates there are threepopular existing types of recommendation systems algorithms, Collaborative Filtering (CF), Content-Based Filtering (CB), andHybrid Recommendation System. The MovieLens dataset with its3 variants was utilized for the purpose of this study. The studiedevaluation methods consider both quantitative and qualitativeaspects of algorithm with many evaluation parameters like meansquared error (MSE), root mean squared error (RMSE), Test Timeand Fit Time are calculated for each recommender algorithmimplementation. The study identifies the gaps and challengesfaced by every recommender algorithm. This study will alsohelp researchers to propose new recommendation algorithms by overcoming identified research gaps and challenges of existing algorithms.