Imputation strategies for cold-start users in NMF-based recommendation systems

Fatemah Alghamedy, Jun Zhang

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

2 Scopus citations

Abstract

We propose a recommendation system method which is based on NMF (Nonnegative Matrix Factorization) in collaborative filtering to enhance the rating predictions. The proposed method conduct selective imputations that fuses the factored original rating matrix and the factored imputed rating matrix into one system. The outputs of the factorized matrices provide four different ways to calculate the predicted ratings which are called sub-predicted ratings. Our proposed method is capable of predicting the rating by utilizing either the imputed users, or imputed items, or both in order to limit the errors that may be introduced from the imputed ratings. We proposed five strategies to calculate the final predicted rating from the sub-predicted ratings. The prediction results of rating values that are not close to the average of the rating values could be enhanced by utilizing the proposed method. Experiments on four different datasets are conducted to examine the proposed approach. The results show that our approach improves the predicted rating especially with Max of value category strategy.

Original languageEnglish
Title of host publicationProceedings of 3rd International Conference on Information System and Data Mining, ICISDM 2019
Pages119-128
Number of pages10
ISBN (Electronic)9781450366359
DOIs
StatePublished - Apr 6 2019
Event3rd International Conference on Information System and Data Mining, ICISDM 2019 - Houston, United States
Duration: Apr 6 2019Apr 8 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd International Conference on Information System and Data Mining, ICISDM 2019
Country/TerritoryUnited States
CityHouston
Period4/6/194/8/19

Bibliographical note

Publisher Copyright:
© 2019 Association for Computing Machinery.

Keywords

  • Collaborative filtering
  • Imputation
  • Nonnegative matrix factorization
  • Recommendation system
  • Trust matrix

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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