An In-Depth Evaluation of Recommendation Systems: Methods, Challenges, and Solutions

Abstract

Recommendation systems (RS) play a vital role in the digital landscape, in shaping user experiences across various platforms. It delves into the origins and key characteristics of Content-Based Filtering and Collaborative Filtering, backed by empirical analysis to underscore their practical significance. It will go through the intricate development stages of RS, spanning from data investigation to prediction methodologies, and tackles challenges such as the cold-start problem. RS is categorized into three main types: collaborative filtering, content-based filtering, and hybrid recommendation systems, highlighting their potential synergy in enhancing recommendation accuracy result and breadth. These insights lay the groundwork for subsequent, which explore evaluation techniques, seminal research, dataset analysis, and experimental findings, concluding with reflections and avenues for future research to advance the field of recommendation systems.