Alternating pointwise-pairwise learning for personalized item ranking

Yu Lei; Wenjie Li; Ziyu Lu; Miao Zhao

doi:10.1145/3132847.3133100

Alternating pointwise-pairwise learning for personalized item ranking

Yu Lei, Wenjie Li, Ziyu Lu, Miao Zhao

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

17 Citations (Scopus)

Abstract

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 language	English
Title of host publication	CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
Publisher	Association for Computing Machinery
Pages	2155-2158
Number of pages	4
Volume	Part F131841
ISBN (Electronic)	9781450349185
DOIs	https://doi.org/10.1145/3132847.3133100
Publication status	Published - 6 Nov 2017
Event	26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Pan Pacific Singapore Hotel, Singapore, Singapore Duration: 6 Nov 2017 → 10 Nov 2017

Conference

Conference	26th ACM International Conference on Information and Knowledge Management, CIKM 2017
Country/Territory	Singapore
City	Singapore
Period	6/11/17 → 10/11/17

Keywords

Collaborative ranking
Item recommendation
Personalized item ranking

ASJC Scopus subject areas

General Business,Management and Accounting
General Decision Sciences

More information

10.1145/3132847.3133100

Cite this

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title = "Alternating pointwise-pairwise learning for personalized item ranking",

abstract = "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.",

keywords = "Collaborative ranking, Item recommendation, Personalized item ranking",

author = "Yu Lei and Wenjie Li and Ziyu Lu and Miao Zhao",

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booktitle = "CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management",

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Lei, Y, Li, W, Lu, Z & Zhao, M 2017, Alternating pointwise-pairwise learning for personalized item ranking. in CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management. vol. Part F131841, Association for Computing Machinery, pp. 2155-2158, 26th ACM International Conference on Information and Knowledge Management, CIKM 2017, Singapore, Singapore, 6/11/17. https://doi.org/10.1145/3132847.3133100

Alternating pointwise-pairwise learning for personalized item ranking. / Lei, Yu; Li, Wenjie; Lu, Ziyu et al.
CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management. Vol. Part F131841 Association for Computing Machinery, 2017. p. 2155-2158.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Lu, Ziyu

AU - Zhao, Miao

PY - 2017/11/6

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AB - 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.

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DO - 10.1145/3132847.3133100

M3 - Conference article published in proceeding or book

VL - Part F131841

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EP - 2158

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Alternating pointwise-pairwise learning for personalized item ranking

Abstract

Conference

Keywords

ASJC Scopus subject areas

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