International Journal of Networked and Distributed Computing

Volume 9, Issue 1, January 2021, Pages 25 - 32

Featured Hybrid Recommendation System Using Stochastic Gradient Descent

Authors
Si Thin Nguyen1, Hyun Young Kwak1, Si Young Lee1, Gwang Yong Gim2, *
1Department of IT Policy and Management, Graduate School, Soongsil University, Seoul, Korea
2Department of Business Administration, Graduate School, Soongsil University, Seoul, Korea
*Corresponding author. Email: gygim@ssu.ac.kr
Corresponding Author
Gwang Yong Gim
Received 9 October 2020, Accepted 18 November 2020, Available Online 5 January 2021.
DOI
https://doi.org/10.2991/ijndc.k.201218.004How to use a DOI?
Keywords
Recommendation system, stochastic gradient, decent matrix factorization, content-based, collaborative filtering, incremental learning
Abstract

Beside cold-start and sparsity, developing incremental algorithms emerge as interesting research to recommendation system in real-data environment. While hybrid system research is insufficient due to the complexity in combining various source of each single such as content-based or collaboration filtering, stochastic gradient descent exposes the limitations regarding optimal process in incremental learning. Stem from these disadvantages, this study adjusts a novel incremental algorithm using in featured hybrid system combing the feature of content-based method and the robustness of matrix factorization in collaboration filtering. To evaluate experiments, the authors simultaneously design an incremental evaluation approach for real data. With the hypothesis results, the study proves that the featured hybrid system is feasible to develop as the future direction research, and the proposed model achieve better results in both learning time and accuracy.

Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Networked and Distributed Computing
Volume-Issue
9 - 1
Pages
25 - 32
Publication Date
2021/01
ISSN (Online)
2211-7946
ISSN (Print)
2211-7938
DOI
https://doi.org/10.2991/ijndc.k.201218.004How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Si Thin Nguyen
AU  - Hyun Young Kwak
AU  - Si Young Lee
AU  - Gwang Yong Gim
PY  - 2021
DA  - 2021/01
TI  - Featured Hybrid Recommendation System Using Stochastic Gradient Descent
JO  - International Journal of Networked and Distributed Computing
SP  - 25
EP  - 32
VL  - 9
IS  - 1
SN  - 2211-7946
UR  - https://doi.org/10.2991/ijndc.k.201218.004
DO  - https://doi.org/10.2991/ijndc.k.201218.004
ID  - Nguyen2021
ER  -