Imputing trust network information in NMF-based collaborative filtering

Fatemah Alghamedy, Xiwei Wang, Jun Zhang

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

2 Scopus citations


We propose an NMF (Nonnegative Matrix Factorization)-based approach in collaborative filtering based recommendation systems to handle the cold-start users issue, especially for the New-Users who did not rate any items. The proposed approach utilizes the trust network information to impute missing ratings before NMF is applied. We do two cases of imputation: (1) when all users are imputed, and (2) when only New-Users are imputed. To study the impact of the imputation, we divide users into three groups and calculate their recommendation errors. Experiments on four different datasets are conducted to examine the proposed approach. The results show that our approach can handle the New-Users issue and reduce the recommendation errors for the whole dataset especially in the second imputation case.

Original languageEnglish
Title of host publicationProceedings of the ACMSE 2018 Conference
ISBN (Electronic)9781450356961
StatePublished - Mar 29 2018
Event2018 Annual ACM Southeast Conference, ACMSE 2018 - Richmond, United States
Duration: Mar 29 2018Mar 31 2018

Publication series

NameProceedings of the ACMSE 2018 Conference


Conference2018 Annual ACM Southeast Conference, ACMSE 2018
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2018 Association for Computing Machinery.


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

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Hardware and Architecture
  • Software


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