skip to main content
10.1145/3640457.3688034acmconferencesArticle/Chapter ViewAbstractPublication PagesrecsysConference Proceedingsconference-collections
extended-abstract

Joint Modeling of Search and Recommendations Via an Unified Contextual Recommender (UniCoRn)

Published: 08 October 2024 Publication History

Abstract

Search and recommendation systems are essential in many services, and they are often developed separately, leading to complex maintenance and technical debt. In this paper, we present a unified deep learning model that efficiently handles key aspects of both tasks.

References

[1]
Moumita Bhattacharya and Sudarshan Lamkhede. 2022. Augmenting Netflix Search with In-Session Adapted Recommendations. In Proceedings of the 16th ACM Conference on Recommender Systems(RecSys ’22). Association for Computing Machinery, New York, NY, USA, 542–545. https://doi.org/10.1145/3523227.3547407
[2]
Sudarshan Lamkhede and Sudeep Das. 2019. Challenges in Search on Streaming Services: Netflix Case Study. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (Paris, France) (SIGIR’19). Association for Computing Machinery, New York, NY, USA, 1371–1374. https://doi.org/10.1145/3331184.3331440
[3]
Sudarshan Dnyaneshwar Lamkhede and Christoph Kofler. 2021. Recommendations and Results Organization in Netflix Search. In Proceedings of the 15th ACM Conference on Recommender Systems (Amsterdam, Netherlands) (RecSys ’21). Association for Computing Machinery, New York, NY, USA, 577–579. https://doi.org/10.1145/3460231.3474602
[4]
Roger Menezes, Rahul Jha, Gary Yeh, and Sudarshan Lamkhede. 2023. Lessons Learnt From Consolidating ML Models in a Large Scale Recommendation System. https://netflixtechblog.medium.com/lessons-learnt-from-consolidating-ml-models-in-a-large-scale-recommendation-system-870c5ea5eb4a
[5]
Vito Ostuni, Christoph Kofler, Manjesh Nilange, Sudarshan Lamkhede, and Dan Zylberglejd. 2023. Search Personalization at Netflix. In Companion Proceedings of the ACM Web Conference 2023(WWW ’23 Companion). Association for Computing Machinery, New York, NY, USA, 756–758. https://doi.org/10.1145/3543873.3587675
[6]
David Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Francois Crespo, and Dan Dennison. 2015. Hidden technical debt in machine learning systems. Advances in neural information processing systems 28 (2015).
[7]
Hamed Zamani and W Bruce Croft. 2018. Joint modeling and optimization of search and recommendation. arXiv preprint arXiv:1807.05631 (2018).

Index Terms

  1. Joint Modeling of Search and Recommendations Via an Unified Contextual Recommender (UniCoRn)

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
    October 2024
    1438 pages
    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

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 08 October 2024

    Check for updates

    Author Tags

    1. Personalization
    2. Recommendations
    3. Search

    Qualifiers

    • Extended-abstract
    • Research
    • Refereed limited

    Conference

    Acceptance Rates

    Overall Acceptance Rate 254 of 1,295 submissions, 20%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 1,415
      Total Downloads
    • Downloads (Last 12 months)1,415
    • Downloads (Last 6 weeks)23
    Reflects downloads up to 18 Feb 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media