RLT: Residual-loop training in collaborative filtering for combining factorization and global-local neighborhood

Lei Li, Weike Pan*, Li CHEN, Zhong Ming

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Collaborative filtering (CF) is an important recommendation problem focusing on predicting users’ future preferences by exploiting their historical tastes. One typical training paradigm for this problem is called residual training (RT), which is usually built on two basic components of factorization- and local neighborhood-based methods in a sequential manner. RT has been well recognized with the ability of achieving higher recommendation accuracy than either factorization- or neighborhood-based method. In this paper, we design a new residual training paradigm called residual-loop training (RLT), which aims to fully exploit the complementarity of factorization, global neighborhood and local neighborhood in one single algorithm. Experimental results on three public datasets show the promising results of our RLT compared with several state-of-the-art methods.

Original languageEnglish
Title of host publicationWeb Services – ICWS 2018 - 25th International Conference, Held as Part of the Services Conference Federation, SCF 2018, Proceedings
EditorsHai Jin, Liang-Jie Zhang, Qingyang Wang
PublisherSpringer Verlag
Pages326-336
Number of pages11
ISBN (Print)9783319942889
DOIs
Publication statusPublished - 2018
Event25th International Conference on Web Services, ICWS 2018 Held as Part of the Services Conference Federation, SCF 2018 - Seattle, United States
Duration: 25 Jun 201830 Jun 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10966 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference25th International Conference on Web Services, ICWS 2018 Held as Part of the Services Conference Federation, SCF 2018
Country/TerritoryUnited States
CitySeattle
Period25/06/1830/06/18

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Collaborative filtering
  • Residual training
  • Residual-loop training

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