Exploring The Efficacy Of Graph-Based Algorithms For Recommendation Systems

Abstract

Recommendation systems play a pivotal role in facilitating personalized user experiences across various online platforms. Graph-based algorithms have emerged as promising approaches for recommendation tasks due to their ability to capture complex relationships and dependencies among users and items. This paper investigates the efficiency of graph- based algorithms in recommendation systems by conducting a comparative analysis of their performance against traditional methods. We explore the strengths and limitations of graph- based approaches in handling diverse recommendation scenarios, including collaborative filtering, content-based filtering, and hybrid methods. Through empirical evaluations using real-world datasets, we assess the effectiveness of graph-based algorithms in terms of recommendation accuracy, scalability, and computational efficiency. Our findings provide insights into the suitability of graph-based approaches for different recommendation tasks and shed light on their potential for improving recommendation system performance.