FeatureMF - A Novel Collaborative Filtering Recommendation Model

Haiyang Zhang, Ivan Ganchev, Nikola S. Nikolov

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

1 Citation (Scopus)

Abstract

This paper1 presents a novel matrix factorization (MF) model, called FeatureMF, which takes into account item features and thus addresses the cold-start item and data sparsity problems of collaborative filtering (CF). More specifically, the model extends item latent vectors with item representation learned from metadata. Experiments conducted on a public dataset with two testing views confirm that FeatureMF achieves better recommendation performance than some of the popular state-of-the-art MF models.

Original languageEnglish
Title of host publicationProceedings - 2nd International Conference on Mathematics and Computers in Science and Engineering, MACISE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages34-37
Number of pages4
ISBN (Electronic)9781728166957
DOIs
Publication statusPublished - Jan 2020
Externally publishedYes
Event2nd International Conference on Mathematics and Computers in Science and Engineering, MACISE 2020 - Madrid, Spain
Duration: 18 Jan 202020 Jan 2020

Publication series

NameProceedings - 2nd International Conference on Mathematics and Computers in Science and Engineering, MACISE 2020

Conference

Conference2nd International Conference on Mathematics and Computers in Science and Engineering, MACISE 2020
Country/TerritorySpain
CityMadrid
Period18/01/2020/01/20

Keywords

  • cold start
  • collaborative filtering (CF)
  • data-sparsity
  • matrix factorization (MF)
  • recommendation model

Cite this