Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Auto Content Moderation in C2C e-Commerce at OpML20

Auto Content Moderation in C2C e-Commerce at OpML20

2020 USENIX Conference on Operational Machine Learning

Authors:
Shunya Ueta, Suganprabu Nagaraja, and Mizuki Sango, Mercari, inc.

Abstract:
Consumer-to-consumer (C2C) e-Commerce is a large and growing industry with millions of monthly active users. In this paper, we propose auto content moderation for C2C e-Commerce to moderate items using Machine Learning (ML). We will also discuss practical knowledge gained from our auto content moderation system. The system has been deployed to production at Mercari since late 2017 and has significantly reduced the operation cost in detecting items violating our policies. This system has increased coverage by 554.8 % over a rule-based approach.

Shunya Ueta

July 27, 2020
Tweet

More Decks by Shunya Ueta

Other Decks in Programming

Transcript

  1. 1
    Auto Content Moderation
    in C2C e-Commerce
    Shunya Ueta, Suganprabu Nagarajan, Mizuki Sango
    (Mercari, inc)
    2020 USENIX Conference
    on Operational Machine Learning
    JULY 27–AUGUST 7, 2020

    View Slide

  2. 2
    1. Content Moderation
    2. Auto Content Moderation in C2C e-Commerce
    3. Task design and model strategy
    4. Offline/online evaluation
    5. System architecture
    6. Business Impact
    Contents

    View Slide

  3. 3
    Identify potentially unsafe or inappropriate content in service
    ● App Discovery with Google Play, Part 3: Machine Learning to Fight Spam and Abuse
    at Scale
    ● YouTube Community Guidelines enforcement
    ● AI advances to better detect hate speech by Facebook
    ● Advances in content understanding, self-supervision to protect people by Facebook
    ● Facebook Transparency Report
    ● A Safe and Secure Marketplace by Mercari
    ● etc.
    Content Moderation

    View Slide

  4. 4
    The Mercari app is a C2C marketplace where individuals can easily sell used items
    What is Mercari?
    Japan
    U.S.
    Monthly active users: 16+ Million
    Total number of items: 1.5+ Billion

    View Slide

  5. 5
    Why Content Moderation in C2C e-Commerce?
    C2C e-Commerce
    Sellers Buyers
    We want to decrease risk for customer and marketplace
    Sellers unintentionally violate policy. Buyers buy violated items without
    knowing
    Policy case: counterfeits, weapons, etc.

    View Slide

  6. 6
    Content Moderation system
    C2C e-Commerce
    Sell items Discover
    Moderator
    Manual review
    Moderation
    Service
    Hide items & Alert
    marketplace
    Sellers Buyers
    screened

    View Slide

  7. 7
    Concept of Moderation Service: Rule based
    Moderation
    Service
    Rule based
    Pros
    ● Easy to develop and can be
    quickly released to production
    Cons
    ● Hard to manage
    ● Difficult to cover the
    inconsistencies in spellings
    e.g. {NIKE, nike, ないき, ナイキ}
    Moderator
    Manual review

    View Slide

  8. 8
    Concept of Moderation Service: ML
    Moderation
    Service
    Rule based
    Pros
    ● Automatically learns the features
    of items deleted by moderators
    ● Adapts to spelling inconsistencies
    Cons
    ● Model update is hard
    ● Concept drift
    (a.k.a. training-serving skew)
    Moderator
    Manual review
    Machine
    Learning

    View Slide

  9. 9
    How to create the data for ML
    Rule based
    Moderator
    Machine
    Learning
    Sell items
    Report items
    Hide items & Alert
    Positive
    Deleted items by Moderator
    Negative
    Not deleted items by Moderator
    Dataset
    Moderation Service
    Review

    View Slide

  10. 10
    Task Design
    ● Data is highly imbalanced
    ● Each violated topic’s total
    number of alerts is bounded
    by moderator team
    All models trained as one-vs-all
    ● No side-effect when deploying
    a trained model to other class
    ● Hard to improve performance
    for each topic in a multi-class
    model
    Negative
    Violated
    Topic A
    Violated
    Topic N
    ...
    Positive
    Model
    A
    Model
    B
    ...
    counterfeits weapons

    View Slide

  11. 11
    Multimodality of content
    Case of items
    Items have multimodal data
    ● Image
    ● Text
    ● Category
    ● Brand
    ● Price, etc.
    We use multimodal model to improve model
    performance.
    See our article:
    https://tech.mercari.com/entry/2019/09/12/130000

    View Slide

  12. 12
    Model selection based on dataset size
    ● Gradient Boosted Decision Trees (GBDT)
    → Efficient for training and inference when training data size is not large
    *Image feature is not used in GBDT
    ● Gated Multimodal Unit (GMU)
    → Potentially most accurate using multimodal data

    View Slide

  13. 13
    Offline evaluation
    Metric is Precision@K: K is the bound on the daily total number of alerts
    in each violated topic decided by Moderators
    2020-07-13
    Current model’s prediction
    result In production
    Top K
    Evaluate new model against current
    model using the same item ids
    item ids same
    as production
    top K
    2020-07-13
    New model’s prediction result
    In test dataset.
    e.g.

    View Slide

  14. 14
    Online evaluation
    → Faster decision making leads to efficient operation
    Current
    Model
    New
    Model
    Same traffic
    Moderator
    Manual review
    Each model alert number: K/2
    Metrics: Precision@K/2
    After a certain time after a
    new model is released, we
    decide which model should
    be deprecated based on the
    above metrics.
    Classic A/B testing can take several months. It was difficult to collect enough
    transactions for t-test.

    View Slide

  15. 15
    Offline/online evaluation result
    Algorithms Offline Online
    GBDT +18.2% Not Released
    GMU +21.2% +23.2%
    Table shows the relative performance gain of
    offline evaluation metric is precision@K ,
    online evaluation metric is precision@K/2
    on one violated topic
    Baseline model is Logistic regression that was already released in production

    View Slide

  16. 16
    Container based Training Pipeline
    Data Load
    Write manifest files containing requirements like CPU, GPU and Storage
    CPU CPU or GPU
    Training Offline
    Evaluation
    CPU
    BigQuery
    BigQuery

    View Slide

  17. 17
    Serving system architecture
    Message
    queue
    Message
    queue
    proxy layer prediction layer
    .
    Preprocessing + inference
    Container
    Pod
    GBDT
    based model
    Preprocessing
    Container
    .
    .
    Proxy
    container
    subscribe
    publish
    Pod
    Inference
    Container
    Caffe2
    Pod
    Deep Learning
    based model
    We manage over 15 Machine Learning models in production
    Pod
    Deep Learning
    based model

    View Slide

  18. 18
    Horizontal Pod Autoscaler by kubernetes
    ● Reliable system: Traffic changes with time,
    HPA can adopt to varying traffic
    ● Cheaper billing cost: Reduce to 1/6 by HPA
    Billing cost transition after applying HPA
    Billing cost
    day
    Each color is each machine learning model

    View Slide

  19. 19
    Impact of Machine Learning system
    Discovered 100 violating items
    Moderator
    Manual review
    Moderation
    Service
    Rule based
    Machine
    Learning
    Hide & Alert
    +Discovered 554 violating items
    Machine Learning system
    has increased coverage by 554% ↑ over rule based approach
    e.g.

    View Slide

  20. 20
    If you have a question to this talk
    First author is Shunya UETA, please e-mail: [email protected]
    Acknowledgements
    Co-Authors: Suganprabu Nagarajan, Mizuki Sango
    Contributter:
    ● Abhishek Vilas Munagekar, Yusuke Shido, Vamshi Teja Racha, Sumit Verma and
    Keisuke Umezawa for their contribute to this system
    ● Dr. Antony for his feedback about the paper
    ● Yushi Kurita, Yuki Ito as Product Manager, All Trust and Safety project member and
    all Customer Service as Moderator to success this project.
    Question and Thanks collaborator

    View Slide