Version 1
: Received: 5 October 2023 / Approved: 5 October 2023 / Online: 6 October 2023 (08:23:50 CEST)
How to cite:
Payandenick, M.; Wang, Y.C. ResNetMF: Enhancing Recommendation Systems with Residual Network Matrix Factorization. Preprints2023, 2023100302. https://doi.org/10.20944/preprints202310.0302.v1
Payandenick, M.; Wang, Y.C. ResNetMF: Enhancing Recommendation Systems with Residual Network Matrix Factorization. Preprints 2023, 2023100302. https://doi.org/10.20944/preprints202310.0302.v1
Payandenick, M.; Wang, Y.C. ResNetMF: Enhancing Recommendation Systems with Residual Network Matrix Factorization. Preprints2023, 2023100302. https://doi.org/10.20944/preprints202310.0302.v1
APA Style
Payandenick, M., & Wang, Y.C. (2023). ResNetMF: Enhancing Recommendation Systems with Residual Network Matrix Factorization. Preprints. https://doi.org/10.20944/preprints202310.0302.v1
Chicago/Turabian Style
Payandenick, M. and Yin Chai Wang. 2023 "ResNetMF: Enhancing Recommendation Systems with Residual Network Matrix Factorization" Preprints. https://doi.org/10.20944/preprints202310.0302.v1
Abstract
In this paper, we introduce ResNetMF, a groundbreaking approach that harnesses the power of residual network matrix factorization to revolutionize recommendation systems. ResNetMF integrates residual networks, renowned for their ability to capture intricate patterns and features, with matrix factorization techniques that excel in modelling user-item interactions. This fusion presents a novel solution that surpasses the limitations of traditional recommendation systems. Through comprehensive experimentation and evaluation of diverse datasets, ResNetMF demonstrates remarkable enhancements in recommendation accuracy and efficiency. By effectively capturing both linear and nonlinear relationships in user-item interactions, ResNetMF provides superior recommendation quality. The outcomes from experiments unequivocally highlight the superiority of ResNetMF over existing state-of-the-art recommendation approaches, thereby validating its innovative nature and underscoring its potential to shape the future of recommendation systems. Through the integration of the deep residual network, ResNetMF approach facilitates the training of neural networks, enabling them to explore the underlying data layers more comprehensively. Extensive experimentation and evaluation across various datasets provide compelling evidence for the superiority of ResNetMF. Moreover, the proposed method utilized natural language processing (NLP) techniques for targeted information dissemination in recommendation systems, emphasizing the importance of personalized and relevant recommendations for user satisfaction and engagement.
Keywords
recommendation systems, personalized recommendations, ResNetMF, Residual Network Matrix Factorization, deep residual network, recommendation accuracy, linear and nonlinear relationships
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
