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2022 | OriginalPaper | Chapter

32. Recommendation System Using MixPMF

Authors : Rohit Gund, James Andro-Vasko, Doina Bein, Wolfgang Bein

Published in: ITNG 2022 19th International Conference on Information Technology-New Generations

Publisher: Springer International Publishing

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Abstract

The chapter introduces a novel recommendation system using MixPMF, a hybrid model that integrates Probabilistic Matrix Factorization (PMF), Constrained PMF (CPMF), and Kernelized PMF (KPMF). It addresses the challenges faced by existing recommendation systems in handling large, sparse, and imbalanced datasets, particularly in the context of music and movie recommendations. The MixPMF model leverages user network information and artist tag data to enhance recommendation accuracy. The chapter provides a comprehensive overview of the architecture, background work, model training, and evaluation of the MixPMF system. It demonstrates the superior performance of MixPMF compared to PMF, CPMF, and KPMF models through RMSE evaluations. The chapter concludes with potential future enhancements, including the development of a GUI with automatic playlist generation and the exploration of parallel processing techniques for scalability.
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Metadata
Title
Recommendation System Using MixPMF
Authors
Rohit Gund
James Andro-Vasko
Doina Bein
Wolfgang Bein
Copyright Year
2022
Book
ITNG 2022 19th International Conference on Information Technology-New Generations

Print ISBN: 978-3-030-97651-4

Electronic ISBN: 978-3-030-97652-1

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-030-97652-1_32

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