Contemporary Recommendation Systems on Big Data and Their Applications: A Survey

Abstract

This survey paper conducts a comprehensive analysis of the evolution and contemporary landscape of recommendation systems, which have been extensively incorporated across a myriad of web applications. It delves into the progression of personalized recommendation methodologies tailored for online products or services, organizing the array of recommendation techniques into four main categories: content-based, collaborative filtering, knowledge-based, and hybrid approaches, each designed to cater to specific contexts. The document provides an in-depth review of both the historical underpinnings and the cutting-edge innovations in the domain of recommendation systems, with a special focus on implementations leveraging big data analytics. The paper also highlights the utilization of prominent datasets such as MovieLens, Amazon Reviews, Netflix Prize, Last.fm, and Yelp in evaluating recommendation algorithms. It further outlines and explores the predominant challenges encountered in the current generation of recommendation systems, including issues related to data sparsity, scalability, and the imperative for diversified recommendation outputs. The survey underscores these challenges as promising directions for subsequent research endeavors within the discipline. Additionally, the paper examines various real-life applications driven by recommendation systems, addressing the hurdles involved in seamlessly integrating these systems into everyday life. Ultimately, the survey underscores how the advancements in recommendation systems, propelled by big data technologies, have the potential to significantly enhance real-world experiences.

34 pages, 8 figures, 2 table

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