research-article

Discrete Ranking-based Matrix Factorization with Self-Paced Learning

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Guowu YangAuthors Info & Claims

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 2758 - 2767

https://doi.org/10.1145/3219819.3220116

Published: 19 July 2018 Publication History

Abstract

The efficiency of top-k recommendation is vital to large-scale recommender systems. Hashing is not only an efficient alternative but also complementary to distributed computing, and also a practical and effective option in a computing environment with limited resources. Hashing techniques improve the efficiency of online recommendation by representing users and items by binary codes. However, objective functions of existing methods are not consistent with ultimate goals of recommender systems, and are often optimized via discrete coordinate descent, easily getting stuck in a local optimum. To this end, we propose a Discrete Ranking-based Matrix Factorization (DRMF) algorithm based on each user's pairwise preferences, and formulate it into binary quadratic programming problems to learn binary codes. Due to non-convexity and binary constraints, we further propose self-paced learning for improving the optimization, to include pairwise preferences gradually from easy to complex. We finally evaluate the proposed algorithm on three public real-world datasets, and show that the proposed algorithm outperforms the state-of-the-art hashing-based recommendation algorithms, and even achieves comparable performance to matrix factorization methods.

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Cited By

Chen JZhang RJiang XHu C(2024)SPContrastNet: A Self-Paced Contrastive Learning Model for Few-Shot Text ClassificationACM Transactions on Information Systems10.1145/365260042:5(1-25)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3652600
Zhou XLin NZheng WZhou DYang A(2024)A contrastive news recommendation framework based on curriculum learningUser Modeling and User-Adapted Interaction10.1007/s11257-024-09422-035:1Online publication date: 28-Dec-2024
https://doi.org/10.1007/s11257-024-09422-0
Luo FWu JWang T(2023)Discrete Listwise Content-aware RecommendationACM Transactions on Knowledge Discovery from Data10.1145/360933418:1(1-20)Online publication date: 10-Aug-2023
https://dl.acm.org/doi/10.1145/3609334
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Index Terms

Discrete Ranking-based Matrix Factorization with Self-Paced Learning
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web searching and information discovery
      1. Collaborative filtering
      2. Personalization

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cover image ACM Other conferences

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2018

2925 pages

ISBN:9781450355520

DOI:10.1145/3219819

General Chairs:
Yike Guo
Imperial College London
,
Faisal Farooq
IBM

Copyright © 2018 ACM.

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Published: 19 July 2018

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KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 19 - 23, 2018

London, United Kingdom

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KDD '18 Paper Acceptance Rate 107 of 983 submissions, 11%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Chen JZhang RJiang XHu C(2024)SPContrastNet: A Self-Paced Contrastive Learning Model for Few-Shot Text ClassificationACM Transactions on Information Systems10.1145/365260042:5(1-25)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3652600
Zhou XLin NZheng WZhou DYang A(2024)A contrastive news recommendation framework based on curriculum learningUser Modeling and User-Adapted Interaction10.1007/s11257-024-09422-035:1Online publication date: 28-Dec-2024
https://doi.org/10.1007/s11257-024-09422-0
Luo FWu JWang T(2023)Discrete Listwise Content-aware RecommendationACM Transactions on Knowledge Discovery from Data10.1145/360933418:1(1-20)Online publication date: 10-Aug-2023
https://dl.acm.org/doi/10.1145/3609334
Kammari MBhavani S(2023)Citation recommendation using modified HITS algorithmComputing10.1007/s00607-023-01213-6106:7(2239-2259)Online publication date: 9-Sep-2023
https://doi.org/10.1007/s00607-023-01213-6
Liu HWei YYin JNie L(2022)HS-GCN: Hamming Spatial Graph Convolutional Networks for RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3158317(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3158317
Le DLauw H(2021)Efficient Retrieval of Matrix Factorization-Based Top-k RecommendationsJournal of Artificial Intelligence Research10.1613/jair.1.1240370(1441-1479)Online publication date: 1-May-2021
https://dl.acm.org/doi/10.1613/jair.1.12403
Jiang GWang HChen JWang HLian DChen EDiaz FShah CSuel TCastells PJones RSakai T(2021)xLightFM: Extremely Memory-Efficient Factorization MachineProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462941(337-346)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462941
Thanthriwatta TRosenblum D(2021)Instance Selection for Online Updating in Dynamic Recommender EnvironmentsAdvances in Knowledge Discovery and Data Mining10.1007/978-3-030-75765-6_49(612-624)Online publication date: 8-May-2021
https://doi.org/10.1007/978-3-030-75765-6_49
Altosaar JRanganath RTansey W(2021)RankFromSets: Scalable set recommendation with optimal recallStat10.1002/sta4.36310:1Online publication date: 22-Mar-2021
https://doi.org/10.1002/sta4.363
Zhang YTsang IDuan L(2020)Collaborative Generative Hashing for Marketing and Fast Cold-Start RecommendationIEEE Intelligent Systems10.1109/MIS.2020.302519735:5(84-95)Online publication date: 1-Sep-2020
https://doi.org/10.1109/MIS.2020.3025197
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