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2024 | OriginalPaper | Buchkapitel

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

verfasst von : Karim Seridi, Abdessamad El Rharras

Erschienen in: Innovations in Smart Cities Applications Volume 7

Verlag: Springer Nature Switzerland

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Abstract

Today, several successful companies like Uber, Airbnb, and others have adopted sharing economy business models. The increasing growth of websites and applications adopting this model pushes companies to develop differentiation strategies. One of the strategies is to use emerging technologies to offer a better customer experience. Recommender systems (RSs) are AI-based solutions that can provide customized recommendations. To implement an RS in a sharing economy platform, this study intends to compare the performance of two recommendation-system approaches based on their accuracy, computation time, and scalability. The Netflix dataset was used to compare matrix factorization and memory-based techniques based on their performances using offline testing. The results of the study indicate that memory-based methods are more accurate for small datasets but have computation time limitations for large datasets. Single-value decomposition methods scale better than memory-based algorithms.

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Vorheriges Kapitel Effectiveness of Different Machine Learning Algorithms in Road Extraction from UAV-Based Point Cloud

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

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

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

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