Alternating pointwise-pairwise learning for personalized item ranking

Yu Lei, Wenjie Li, Ziyu Lu, Miao Zhao

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

11 Citations (Scopus)


Pointwise and pairwise collaborative ranking are two major classes of algorithms for personalized item ranking. This paper proposes a novel joint learning method named alternating pointwise-pairwise learning (APPL) to improve ranking performance. APPL combines the ideas of both pointwise and pairwise learning, and is able to produce a more effective prediction model. The extensive experiments with both explicit and implicit feedback settings on four real-world datasets demonstrate that APPL performs significantly better than the state-of-the-art methods.
Original languageEnglish
Title of host publicationCIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Number of pages4
VolumePart F131841
ISBN (Electronic)9781450349185
Publication statusPublished - 6 Nov 2017
Event26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Pan Pacific Singapore Hotel, Singapore, Singapore
Duration: 6 Nov 201710 Nov 2017


Conference26th ACM International Conference on Information and Knowledge Management, CIKM 2017


  • Collaborative ranking
  • Item recommendation
  • Personalized item ranking

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

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