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A Novel Hybrid Recommendation System Integrating Content-Based and Rating Information

  • Tan Nghia DuongEmail author
  • Viet Duc Than
  • Tuan Anh Vuong
  • Trong Hiep Tran
  • Quang Hieu Dang
  • Duc Minh Nguyen
  • Hung Manh Pham
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1036)

Abstract

Collaborative filtering (CF), the most efficient technique in recommendation systems, can be classified into two types: neighborhood-based model and latent factor model. Both are only based on the user-item interaction, or rating information, and do not take into account the item’s content-based information which may contain valuable knowledge. In this work, we propose a hybrid content-based and neighborhood-based recommendation system which utilizes the genome tag associated with each movie in the MovieLens 20M dataset. Experiment results show that our proposed system not only achieves a comparable accuracy but also performs at least 2 times faster than the “pure” CF methods.

Keywords

Recommendation system Collaborative filtering Similarity measure Neighborhood-based Matrix factorization 

Notes

Acknowledgement

This research is funded by Ministry of Science and Technology (MOST) under grant number 10/2018/ĐTCT-KC.01.14/16-20.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Tan Nghia Duong
    • 1
    Email author
  • Viet Duc Than
    • 1
  • Tuan Anh Vuong
    • 1
  • Trong Hiep Tran
    • 1
  • Quang Hieu Dang
    • 1
  • Duc Minh Nguyen
    • 1
  • Hung Manh Pham
    • 1
  1. 1.Hanoi University of Science and TechnologyHanoiVietnam

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