skip to main content
10.1145/3616855.3635749acmconferencesArticle/Chapter ViewAbstractPublication PageswsdmConference Proceedingsconference-collections
abstract

Fresh Content Recommendation at Scale: A Multi-funnel Solution and the Potential of LLMs

Published: 04 March 2024 Publication History

Abstract

Recommendation system serves as a conduit connecting users to an incredibly large, diverse and ever growing collection of contents. In practice, missing information on fresh contents needs to be filled in order for them to be exposed and discovered by their audience. In this context, we are delighted to share our success stories in building a dedicated fresh content recommendation stack on a large commercial platform and also shed a light on the utilization of Large Language Models (LLMs) for fresh content recommendations within an industrial framework. To nominate fresh contents, we built a multi-funnel nomination system that combines (i) a two-tower model with strong generalization power for coverage, and (ii) a sequence model with near real-time update on user feedback for relevance, which effectively balances between coverage and relevance. Beyond that, by harnessing the reasoning and generalization capabilities of LLMs, we are presented with exciting prospects to enhance recommendation systems. We share our initial efforts on employing LLMs as data augmenters to bridge the knowledge gap on cold-start items during the training phase. This innovative approach circumvents the costly generation process during inference, presenting a model-agnostic, forward-looking solution for fresh content recommendation.

References

[1]
Youtube Official Blog. 2023. YouTube by the Number. https://blog.youtube/press/ Retrieved January, 2023 from
[2]
Minmin Chen, Yuyan Wang, Can Xu, Ya Le, Mohit Sharma, Lee Richardson, Su-Lin Wu, and Ed Chi. 2021. Values of User Exploration in Recommender Systems. In RecSys.
[3]
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022. Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5). In RecSys.
[4]
Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, and Qing He. 2020. A survey on knowledge graph-based recommender systems. TKDE.
[5]
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In WWW.
[6]
Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. 2023. Large language models are zero-shot rankers for recommender systems. arXiv preprint arXiv:2305.08845 (2023).
[7]
Tim Ingham. 2023. Over 60,000 Tracks are Now Uploaded to Spotify Every Day. That's Nearly One per Second. https://www.musicbusinessworldwide.com/over-60000-tracks-are-now-uploaded-to-spotify-daily-thats-nearly-one-per-second/ Retrieved January, 2023 from
[8]
Joonseok Lee and Sami Abu-El-Haija. 2017. Large-scale content-only video recommendation. In Proceedings of the IEEE International Conference on Computer Vision Workshops. 987--995.
[9]
Jiacheng Li, Ming Wang, Jin Li, Jinmiao Fu, Xin Shen, Jingbo Shang, and Julian McAuley. 2023. Text Is All You Need: Learning Language Representations for Sequential Recommendation. KDD (2023).
[10]
Jinming Li, Wentao Zhang, Tian Wang, Guanglei Xiong, Alan Lu, and Gerard Medioni. 2023. GPT4Rec: A generative framework for personalized recommendation and user interests interpretation. arXiv preprint arXiv:2304.03879 (2023).
[11]
Siwei Liu, Iadh Ounis, Craig Macdonald, and Zaiqiao Meng. 2020. A heterogeneous graph neural model for cold-start recommendation. In SIGIR.
[12]
Jianling Wang, Kaize Ding, and James Caverlee. 2021a. Sequential Recommendation for Cold-start Users with Meta Transitional Learning. In SIGIR.
[13]
Jianling Wang, Kaize Ding, Liangjie Hong, Huan Liu, and James Caverlee. 2020. Next-item recommendation with sequential hypergraphs. In SIGIR.
[14]
Jianling Wang, Haokai Lu, Sai Zhang, Bart Locanthi, Haoting Wang, Dylan Greaves, Benjamin Lipshitz, Sriraj Badam, Ed H Chi, Cristos J Goodrow, et al. 2023. Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation. In KDD.
[15]
Jianling Wang, Ainur Yessenalina, and Alireza Roshan-Ghias. 2021b. Exploring Heterogeneous Metadata for Video Recommendation with Two-tower Model. arXiv preprint arXiv:2109.11059 (2021).
[16]
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. CSUR (2019).
[17]
Yujia Zheng, Siyi Liu, Zekun Li, and Shu Wu. 2020. Cold-start Sequential Recommendation via Meta Learner. In AAAI.

Cited By

View all
  • (2024)Metadata and Review-Based Hybrid Apparel Recommendation System Using Cascaded Large Language ModelsIEEE Access10.1109/ACCESS.2024.346279312(140053-140071)Online publication date: 2024

Index Terms

  1. Fresh Content Recommendation at Scale: A Multi-funnel Solution and the Potential of LLMs

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data Mining
    March 2024
    1246 pages
    ISBN:9798400703713
    DOI:10.1145/3616855
    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: 04 March 2024

    Check for updates

    Author Tags

    1. cold-start recommendation
    2. hybrid recommendation systems
    3. large language models

    Qualifiers

    • Abstract

    Conference

    WSDM '24

    Acceptance Rates

    Overall Acceptance Rate 498 of 2,863 submissions, 17%

    Upcoming Conference

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)197
    • Downloads (Last 6 weeks)5
    Reflects downloads up to 17 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Metadata and Review-Based Hybrid Apparel Recommendation System Using Cascaded Large Language ModelsIEEE Access10.1109/ACCESS.2024.346279312(140053-140071)Online publication date: 2024

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media