Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, Datasets

Abstract

Recommender systems become essential with the presence of the internet and social media. The perceived benefits of the recommender system can make it easier for users to find suitable products and recommend other products, specifically with lots of information. Recommender systems continue to develop over time. It has led many researchers to continue to find the latest approach and evaluation techniques by comparing the performance of previously existing recommender systems. The main approaches that are often used in recommender systems are Content-Based Filtering (CBF), Collaborative Filtering (CF), and Hybrid Filtering (HBF). This time, we focus on conducting a Systematic Literature Review (SLR) of several research articles and analyzing methods for algorithms developed in building recommender systems. The SLR method consists of three stages: planning, implementation, and reporting. The research used as a comparison is between 2019 and 2023 using various existing data sets. There were 72 primary studies, of which 46 employed the Collaborative Filtering approach, 11 used Content-Based filtering, and 15 used Hybrid Filtering. The results of this SLR process show the advantages and disadvantages of each method and type of evaluation developed in building a recommender system. Apart from that, several challenges arise with various existing problems. However, the model-based collaborative filtering method is one method that can minimize the problems of cold start, data sparsity, and scalability.