skip to main content
10.1145/2507157.2507210acmconferencesArticle/Chapter ViewAbstractPublication PagesrecsysConference Proceedingsconference-collections
research-article
Open access

Learning to rank recommendations with the k-order statistic loss

Published: 12 October 2013 Publication History

Abstract

Making recommendations by learning to rank is becoming an increasingly studied area. Approaches that use stochastic gradient descent scale well to large collaborative filtering datasets, and it has been shown how to approximately optimize the mean rank, or more recently the top of the ranked list. In this work we present a family of loss functions, the k-order statistic loss, that includes these previous approaches as special cases, and also derives new ones that we show to be useful. In particular, we present (i) a new variant that more accurately optimizes precision at k, and (ii) a novel procedure of optimizing the mean maximum rank, which we hypothesize is useful to more accurately cover all of the user's tastes. The general approach works by sampling N positive items, ordering them by the score assigned by the model, and then weighting the example as a function of this ordered set. Our approach is studied in two real-world systems, Google Music and YouTube video recommendations, where we obtain improvements for computable metrics, and in the YouTube case, increased user click through and watch duration when deployed live on www.youtube.com.

References

[1]
J. Davidson, B. Liebald, J. Liu, P. Nandy, T. Van Vleet, U. Gargi, S. Gupta, Y. He, M. Lambert, B. Livingston, et al. The youtube video recommendation system. In Proceedings of the fourth ACM conference on Recommender systems, pages 293--296. ACM, 2010.
[2]
K. Goldberg, T. Roeder, D. Gupta, and C. Perkins. Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval, 4(2):133--151, 2001.
[3]
D. Grangier and S. Bengio. A discriminative kernel-based model to rank images from text queries. PAMI, 30:1371--1384, 2008.
[4]
R. Herbrich, T. Graepel, and K. Obermayer. Large margin rank boundaries for ordinal regression. NIPS, pages 115--132, 1999.
[5]
Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, N. Oliver, and A. Hanjalic. Climf: learning to maximize reciprocal rank with collaborative less-is-more filtering. In Proceedings of the sixth ACM conference on Recommender systems, pages 139--146. ACM, 2012.
[6]
N. Usunier, D. Buffoni, and P. Gallinari. Ranking with ordered weighted pairwise classification. ICML, 2009.
[7]
M. Weimer, A. Karatzoglou, Q. Le, A. Smola, et al. Cofirank-maximum margin matrix factorization for collaborative ranking. NIPS, 2007.
[8]
J. Weston, S. Bengio, and N. Usunier. Wsabie: Scaling up to large vocabulary image annotation. In IJCAI, pages 2764--2770, 2011.
[9]
J. Weston, C. Wang, R. Weiss, and A. Berenzweig. Latent collaborative retrieval. ICML, 2012.
[10]
F. Xia, T.-Y. Liu, J. Wang, W. Zhang, and H. Li. Listwise approach to learning to rank: theory and algorithm. In ICML, 2008.
[11]
Y. Yue, T. Finley, F. Radlinski, and T. Joachims. A support vector method for optimizing average precision. In SIGIR, pages 271--278, 2007.

Cited By

View all
  • (2024)Enhancing Recommender Systems: A Practical Study on Loss Function Optimization in B2B Commerce2024 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD)10.1109/ICTMOD63116.2024.10878142(1-7)Online publication date: 4-Nov-2024
  • (2024)Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosisBriefings in Bioinformatics10.1093/bib/bbae03525:2Online publication date: 12-Feb-2024
  • (2024)CMC-MMR: multi-modal recommendation model with cross-modal correctionJournal of Intelligent Information Systems10.1007/s10844-024-00848-x62:5(1187-1211)Online publication date: 1-Oct-2024
  • Show More Cited By

Index Terms

  1. Learning to rank recommendations with the k-order statistic loss

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    RecSys '13: Proceedings of the 7th ACM conference on Recommender systems
    October 2013
    516 pages
    ISBN:9781450324090
    DOI:10.1145/2507157
    • General Chairs:
    • Qiang Yang,
    • Irwin King,
    • Qing Li,
    • Program Chairs:
    • Pearl Pu,
    • George Karypis
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Sponsors

    In-Cooperation

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 12 October 2013

    Check for updates

    Author Tags

    1. collaborative filtering
    2. learning to rank
    3. loss functions
    4. matrix factorization
    5. stochastic gradient

    Qualifiers

    • Research-article

    Conference

    RecSys '13
    Sponsor:

    Acceptance Rates

    RecSys '13 Paper Acceptance Rate 32 of 136 submissions, 24%;
    Overall Acceptance Rate 254 of 1,295 submissions, 20%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)374
    • Downloads (Last 6 weeks)26
    Reflects downloads up to 20 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Enhancing Recommender Systems: A Practical Study on Loss Function Optimization in B2B Commerce2024 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD)10.1109/ICTMOD63116.2024.10878142(1-7)Online publication date: 4-Nov-2024
    • (2024)Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosisBriefings in Bioinformatics10.1093/bib/bbae03525:2Online publication date: 12-Feb-2024
    • (2024)CMC-MMR: multi-modal recommendation model with cross-modal correctionJournal of Intelligent Information Systems10.1007/s10844-024-00848-x62:5(1187-1211)Online publication date: 1-Oct-2024
    • (2024)Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender SystemsAdvances in Information Retrieval10.1007/978-3-031-56027-9_9(140-156)Online publication date: 24-Mar-2024
    • (2023)How Important is Periodic Model update in Recommender System?Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591934(2661-2668)Online publication date: 19-Jul-2023
    • (2022)Endowing third-party libraries recommender systems with explicit user feedback mechanisms2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER53432.2022.00099(817-821)Online publication date: Mar-2022
    • (2021)Scalable Personalised Item Ranking through Parametric Density EstimationProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462933(921-931)Online publication date: 11-Jul-2021
    • (2021)Improving random walk rankings with feature selection and imputation Science4cast competition, team Hash Brown2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671785(5824-5827)Online publication date: 15-Dec-2021
    • (2021)Differentiable Ranking Metric Using Relaxed Sorting for Top-K RecommendationIEEE Access10.1109/ACCESS.2021.31053899(114649-114658)Online publication date: 2021
    • (2021)DiffGNN: Capturing Different Behaviors in Multiplex Heterogeneous Networks for RecommendationArtificial Intelligence10.1007/978-3-030-93046-2_2(15-26)Online publication date: 5-Jun-2021
    • Show More Cited By

    View Options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Login options

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media