Matching novelty while training: Novel recommendation based on personalized pairwise loss weighting

Kachun Lo, Tsukasa Ishigaki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Most works of recommender system seek to provide highly accurate item prediction while having potentially great bias to popular items. Both users and items' providers will suffer if their system has strong preference for monotonous popular items. A better system should consider also item novelty. Previous works of novel recommendation focus mainly on re-ranking a top-N list generated by an accuracy-focused base model. As a result, these frameworks are 2-stage and essentially limited to the base model. In addition, when training the base model, the common BRP loss function treats all pairs in the same manner, consistently suppresses interesting negative items which should have been recommended. In this work, we propose a personalized pairwise novelty weighting for BPR loss function, which covers the limitations of BPR and effectively improves novelty with marginal loss in accuracy. Base model will be guided by the loss weights to learn user preference and to generate novel suggestion list in 1 stage. Comprehensive experiments on 3 public datasets show that our approach effectively promotes novelty with almost no decrease in accuracy.

Original languageEnglish
Title of host publicationProceedings - 19th IEEE International Conference on Data Mining, ICDM 2019
EditorsJianyong Wang, Kyuseok Shim, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages468-477
Number of pages10
ISBN (Electronic)9781728146034
DOIs
Publication statusPublished - 2019 Nov
Event19th IEEE International Conference on Data Mining, ICDM 2019 - Beijing, China
Duration: 2019 Nov 82019 Nov 11

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2019-November
ISSN (Print)1550-4786

Conference

Conference19th IEEE International Conference on Data Mining, ICDM 2019
CountryChina
CityBeijing
Period19/11/819/11/11

Keywords

  • Loss Weighting
  • Novel Recommendation
  • Personalized Recommendation
  • Recommender System

ASJC Scopus subject areas

  • Engineering(all)

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