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2024 | OriginalPaper | Chapter

A Comparative Analysis of Memory-Based and Model-Based Collaborative Filtering on Recommender System Implementation

Authors : Karim Seridi, Abdessamad El Rharras

Published in: Innovations in Smart Cities Applications Volume 7

Publisher: Springer Nature Switzerland

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Abstract

The chapter delves into the transformative influence of the sharing economy and the crucial role of recommender systems (RS) in enhancing user experiences. It categorizes RSs into memory-based and model-based methods, highlighting the strengths and limitations of each. Memory-based methods, such as cosine similarity, excel in capturing user preferences but are computationally intensive. Model-based methods, like Singular Value Decomposition (SVD), offer superior scalability and accuracy, especially with larger datasets. The study evaluates these approaches using the Netflix dataset, showcasing the trade-offs between accuracy and computational resources. The analysis reveals that while memory-based methods improve accuracy with larger datasets, they demand significant computational power. Conversely, model-based methods like SVD strike a balance between accuracy and scalability, making them suitable for larger datasets. The findings provide valuable insights for practitioners to select the most appropriate RS method for their specific applications.

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Title: A Comparative Analysis of Memory-Based and Model-Based Collaborative Filtering on Recommender System Implementation
Authors: Karim Seridi
Abdessamad El Rharras
Publisher: Springer Nature Switzerland
Book: Innovations in Smart Cities Applications Volume 7
Print ISBN: 978-3-031-54375-3

Electronic ISBN: 978-3-031-54376-0

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-3-031-54376-0_7

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