Comparison of the Efficiency of Different Algorithms on Recommendation System Design: a Case Study

TL;DR

This paper compares the efficiency of various recommendation algorithms, including collaborative filtering and content-based filtering, on a large dataset to identify the most suitable approach based on dataset structure and developer goals.

Contribution

It provides a comparative analysis of different recommendation algorithms using a case study on a large dataset, highlighting their strengths, challenges, and evaluation criteria.

Findings

Collaborative filtering and content-based filtering show different performance patterns.

Algorithm effectiveness varies with dataset structure and application goals.

Challenges include data sparsity and algorithm scalability.

Abstract

By the growing trend of online shopping and e-commerce websites, recommendation systems have gained more importance in recent years in order to increase the sales ratios of companies. Different algorithms on recommendation systems are used and every one produce different results. Every algorithm on this area have positive and negative attributes. The purpose of the research is to test the different algorithms for choosing the best one according as structure of dataset and aims of developers. For this purpose, threshold and k-means based collaborative filtering and content-based filtering algorithms are utilized on the dataset contains 100*73421 matrix length. What are the differences and effects of these different algorithms on the same dataset? What are the challenges of the algorithms? What criteria are more important in order to evaluate a recommendation systems? In the study, we answer these crucial problems with the case study.