short-paper

Alternating Pointwise-Pairwise Learning for Personalized Item Ranking

Authors:

Miao ZhaoAuthors Info & Claims

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

Pages 2155 - 2158

https://doi.org/10.1145/3132847.3133100

Published: 06 November 2017 Publication History

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.

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Cited By

Liu BWang B(2023)Pairwise learning for personalized ranking with noisy comparisonsInformation Sciences: an International Journal10.1016/j.ins.2022.12.028623:C(242-257)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.ins.2022.12.028
Liu JYang ZLi TWu DWang R(2022)SPRKnowledge-Based Systems10.1016/j.knosys.2021.107828239:COnline publication date: 5-Mar-2022
https://dl.acm.org/doi/10.1016/j.knosys.2021.107828
Jurewicz MDerczynski L(2021)Set-to-Sequence Methods in Machine LearningJournal of Artificial Intelligence Research10.1613/jair.1.1283971(885-924)Online publication date: 10-Sep-2021
https://dl.acm.org/doi/10.1613/jair.1.12839
Show More Cited By

Index Terms

Alternating Pointwise-Pairwise Learning for Personalized Item Ranking
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Learning to rank
    2. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering

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Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

November 2017

2604 pages

ISBN:9781450349185

DOI:10.1145/3132847

General Chairs:
Ee-Peng Lim
Singapore Management University, Singapore
,
Marianne Winslett
University of Illinois at Urbana-Champaign, USA, and Advanced Digital Sciences Center, Singapore
,
Program Chairs:
Mark Sanderson
RMIT, Australia
,
Ada Fu
Chinese University of Hong Kong, Hong Kong
,
Jimeng Sun
Georgia Tech, USA
,
Shane Culpepper
RMIT, Australia
,
Eric Lo
Chinese University of Hong Kong, Hong Kong
,
Joyce Ho
Emory University, USA
,
Debora Donato
Mix Tech, Inc., USA
,
Rakesh Agrawal
Data Insights Laboratories, USA
,
Yu Zheng
Microsoft Research Asia, China
,
Carlos Castillo
Qatar Computing Research Institute, Qatar
,
Aixin Sun
Nanyang Technological University, Singapore
,
Vincent S. Tseng
National Cheng Kung University, Taiwan
,
Chenliang Li
Wuhan University, China

Copyright © 2017 ACM.

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Publication History

Published: 06 November 2017

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Funding Sources

National Natural Science Foundation of China
The Hong Kong Polytechnic University
Research Grants Council of Hong Kong

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CIKM '17

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CIKM '17: ACM Conference on Information and Knowledge Management

November 6 - 10, 2017

Singapore, Singapore

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CIKM '17 Paper Acceptance Rate 171 of 855 submissions, 20%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Citations

Cited By

Liu BWang B(2023)Pairwise learning for personalized ranking with noisy comparisonsInformation Sciences: an International Journal10.1016/j.ins.2022.12.028623:C(242-257)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.ins.2022.12.028
Liu JYang ZLi TWu DWang R(2022)SPRKnowledge-Based Systems10.1016/j.knosys.2021.107828239:COnline publication date: 5-Mar-2022
https://dl.acm.org/doi/10.1016/j.knosys.2021.107828
Jurewicz MDerczynski L(2021)Set-to-Sequence Methods in Machine LearningJournal of Artificial Intelligence Research10.1613/jair.1.1283971(885-924)Online publication date: 10-Sep-2021
https://dl.acm.org/doi/10.1613/jair.1.12839
Liu YWang YGao LGuo CXie YXiao Z(2021)Deep Hash-based Relevance-aware Data Quality Assessment for Image Dark DataACM/IMS Transactions on Data Science10.1145/34200382:2(1-26)Online publication date: 8-Apr-2021
https://dl.acm.org/doi/10.1145/3420038
Li LPan WMing Z(2020)CoFi-pointsACM Transactions on Intelligent Systems and Technology10.1145/338912711:4(1-24)Online publication date: 25-May-2020
https://dl.acm.org/doi/10.1145/3389127
Cinar YRenders J(2020)Adaptive Pointwise-Pairwise Learning-to-Rank for Content-based Personalized RecommendationProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3412229(414-419)Online publication date: 22-Sep-2020
https://dl.acm.org/doi/10.1145/3383313.3412229
Yang SGuo CYang B(2020)Context-Aware Path Ranking in Road NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.3025024(1-1)Online publication date: 2020
https://doi.org/10.1109/TKDE.2020.3025024
Banerjee SBanjare PPal BJenamani M(2020)A multistep priority-based ranking for top-N recommendation using social and tag informationJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-020-02388-y12:2(2509-2525)Online publication date: 4-Aug-2020
https://doi.org/10.1007/s12652-020-02388-y
Salminen JCorporan JMarttila RSalenius TJansen B(2019)Using Machine Learning to Predict Ranking of Webpages in the Gift IndustryProceedings of the 9th International Conference on Information Systems and Technologies10.1145/3361570.3361578(1-8)Online publication date: 24-Mar-2019
https://dl.acm.org/doi/10.1145/3361570.3361578
Yi JHuang JQin JLuo Y(2019)Joint Heterogeneous Pair-wise Loss For Top-N RecommendationIEEE/WIC/ACM International Conference on Web Intelligence10.1145/3350546.3352517(188-191)Online publication date: 14-Oct-2019
https://dl.acm.org/doi/10.1145/3350546.3352517
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