A Comprehensive Review on Non-Neural Networks Collaborative Filtering Recommendation Systems

Authors

  • Carmel Wenga Université de la Polynésie Française, Tahiti, French Polynesia
  • Majirus Fansi NzhinuSoft, Evry, Grand Paris Sud, France
  • Sébastien Chabrier Université de la Polynésie Française, Tahiti, French Polynesia
  • Jean-Martial Mari Université de la Polynésie Française, Tahiti, French Polynesia
  • Alban Gabillon Université de la Polynésie Française, Tahiti, French Polynesia

Keywords:

Recommendation systems, collaborative filtering, user-based collaborative filtering, item-based collaborative filtering, matrix factorization, non-negative matrix factorization, explainable matrix factorization, evaluation metrics

Abstract

Over the past two decades, recommendation systems have attracted a lot of interest due to the massive rise of online applications. A particular attention has been paid to collaborative filtering, which is the most widely used in applications that involve information recommendations. Collaborative Filtering (CF) uses the known preference of a group of users to make predictions and recommendations about the unknown preferences of other users (recommendations are made based on the past behavior of users). First introduced in the 1990s, a wide variety of increasingly successful models have been proposed. Due to the success of machine learning techniques in many areas, there has been a growing emphasis on the application of such algorithms in recommendation systems. In this article, we present an overview of the CF approaches for recommendation systems, their two main categories, and their evaluation metrics. We focus on the application of classical Machine Learning algorithms to CF recommendation systems by presenting their evolution from their first use-cases to advanced Machine Learning models. We attempt to provide a comprehensive and comparative overview of CF systems (with python implementations) that can serve as a guideline for research and practice in this area.

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Author Biographies

Carmel Wenga, Université de la Polynésie Française, Tahiti, French Polynesia

Carmel Wenga is a PhD student at the University of French Polynesia. He obtained his master’s degree in Networks and Distributed Systems, his main field is machine learning applied to recommender systems. He is an active member of the NzhinuSoft research team and is actively involved in the development of the recommendation system of ShoppingList, an e-commerce application of NzhinuSoft and deployed in Cameroon. He also works as a Data Engineering & Middleware Developer Consultant for NzhinuSoft.

Majirus Fansi, NzhinuSoft, Evry, Grand Paris Sud, France

Majirus Fansi is Software Architect and CEO at NzhinSoft. He Holds a PhD in computer science from Université de Pau et des Pays de l’Adour and a MBA degree from Stockholm Business School. He has founded NzhinuSoft, on the promise of valorizing enterprise data through data engineering, Data Science, data Visualization, and Application development. His current research interest is recommender systems based on images. First results are implemented on shoppinglist.cm, a market place developed by NzhinuSoft.

Sébastien Chabrier, Université de la Polynésie Française, Tahiti, French Polynesia

Sébastien Chabrier is Associate Professor in Computer Science at the University of French Polynesia working on image processing and machine learning. In year 2003 he received his PhD in image processing and machine learning and in 2021 his French “Habilitation to Lead Research” (HDR) from the University of French Polynesia.

Jean-Martial Mari, Université de la Polynésie Française, Tahiti, French Polynesia

Jean-Martial Mari is Associate Professor in Computer Science at the University of French Polynesia working on signal and image processing. In year 2000 he received the Electrical Engineering diploma of the INSA Lyon, France, with a specialization in Signal Processing, and a Master degree (DEA) in signal and image processing. In 2004 he received his PhD in Acoustics and in 2012 his French “Habilitation to Lead Research” (HDR) from the Université Claude Bernard Lyon 1 (France).

Alban Gabillon, Université de la Polynésie Française, Tahiti, French Polynesia

Alban Gabillon is a full Professor of computer science at the University of French Polynesia. His main research interests are in computer security since his PhD thesis in 1995. He has more recently diversified his activities in the field of machine learning. He is the author or co-author of more than 100 research papers.

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2023-02-08

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Wenga, C., Fansi, M., Chabrier, S., Mari, J.-M., & Gabillon, A. (2023). A Comprehensive Review on Non-Neural Networks Collaborative Filtering Recommendation Systems. Journal of Machine Learning Theory, Applications and Practice, 1(01), 1–44. Retrieved from https://journal.riverpublishers.com/index.php/JMLTAP/article/view/9

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2023: Vol 1

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