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.

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
   

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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
   

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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
   

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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
   

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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.
   

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

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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
   

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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
   

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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
   

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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
    

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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
    

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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
    

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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.
    

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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.
    

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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
    

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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
    

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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
    

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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
    

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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.
    

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20. 20
    If you have a question to this talk
    First author is Shunya UETA, please e-mail: hurutoriya@mercari.com
    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
    

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