A novel evolution-based recommendation system

Yi Cheng Chen, Yen Lung Chu, Lin Hui, Sheng Chih Chen, Tipajin Thaipisutikul, Kai Ze Weng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Matrix factorization (MF) technique has been widely utilized in recommendation systems due to the precise prediction of users' interests. Prior MF-based methods adapt the overall rating to make the recommendation by extracting latent factors from users and items. However, in real applications, people's preferences usually vary with time; the traditional MF-based methods could not properly capture the change of users' interests. In this paper, by incorporating the recurrent neural network (RNN) into MF, we develop a novel recommendation system, M-RNN-F, to effectively describe the preference evolution of users over time. A learning model is proposed to capture the evolution pattern and predict the user preference in the future. The experimental results show that M-RNN-F performs better than other state-of-the-art recommendation algorithms. In addition, we conduct the experiments on real world dataset to demonstrate the practicability.

Original languageEnglish
Title of host publicationProceedings - 2019 12th International Conference on Ubi-Media Computing, Ubi-Media 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages42-46
Number of pages5
ISBN (Electronic)9781728128207
DOIs
StatePublished - Aug 2019
Event12th International Conference on Ubi-Media Computing, Ubi-Media 2019 - Bali, Indonesia
Duration: 6 Aug 20199 Aug 2019

Publication series

NameProceedings - 2019 12th International Conference on Ubi-Media Computing, Ubi-Media 2019

Conference

Conference12th International Conference on Ubi-Media Computing, Ubi-Media 2019
Country/TerritoryIndonesia
CityBali
Period6/08/199/08/19

Keywords

  • Deep learning
  • Long short-term memory
  • Matrix factorization
  • Recommendation system

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