Two-level matrix factorization for recommender systems

Fangfang Li*, Guandong Xu, Longbing Cao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

Many existing recommendation methods such as matrix factorization (MF) mainly rely on user–item rating matrix, which sometimes is not informative enough, often suffering from the cold-start problem. To solve this challenge, complementary textual relations between items are incorporated into recommender systems (RS) in this paper. Specifically, we first apply a novel weighted textual matrix factorization (WTMF) approach to compute the semantic similarities between items, then integrate the inferred item semantic relations into MF and propose a two-level matrix factorization (TLMF) model for RS. Experimental results on two open data sets not only demonstrate the superiority of TLMF model over benchmark methods, but also show the effectiveness of TLMF for solving the cold-start problem.

Original languageEnglish
Pages (from-to)2267-2278
Number of pages12
JournalNeural Computing and Applications
Volume27
Issue number8
DOIs
Publication statusPublished - Nov 2016
Externally publishedYes

Keywords

  • Latent factor model
  • Matrix factorization
  • Recommender system
  • Textual semantic relation

Fingerprint

Dive into the research topics of 'Two-level matrix factorization for recommender systems'. Together they form a unique fingerprint.

Cite this