Weighted matrix factorization with Bayesian personalized ranking

Haiyang Zhang, Ivan Ganchev, Nikola S. Nikolov, Zhanlin Ji, Mairtin O'Droma

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

5 Citations (Scopus)

Abstract

This paper proposes an improvement to item recommendation systems based on collaborative filtering (CF) with implicit feedback data. Combined with the Bayesian Personalized Ranking (BPR) optimization approach, recommended for implicit-only feedback contexts, CF has been shown to be effective in generating accurate recommendations. The method, based on the assumption that a user prefers a consumed item to an unconsumed item, aims to maximize the difference of predicted scores between these items for each user. In most of the existing CF recommendation methods, all items are assigned the same weight, which of course is not the case in reality. In this paper, a new improved matrix factorization (MF) approach is proposed where the weights of items are allowed to vary and be reflective of items' importance or their desirability to a user. The scheme integrates these item weights as appropriate and utilizes a dynamic learning model where learning is driven by BPR. The performance of the proposed method is tested against the traditional MF. Tests confirm that better accuracy can be indeed achieved by the proposed method.

Original languageEnglish
Title of host publicationProceedings of Computing Conference 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages307-311
Number of pages5
ISBN (Electronic)9781509054435
DOIs
Publication statusPublished - 8 Jan 2018
Externally publishedYes
Event2017 SAI Computing Conference 2017 - London, United Kingdom
Duration: 18 Jul 201720 Jul 2017

Publication series

NameProceedings of Computing Conference 2017
Volume2018-January

Conference

Conference2017 SAI Computing Conference 2017
Country/TerritoryUnited Kingdom
CityLondon
Period18/07/1720/07/17

Keywords

  • Bayesian Personalized Ranking
  • collaborative filtering
  • implicit feedback
  • item recommendation
  • matrix factorization

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