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    Machine Learning Model for Predicting Likelihoods of Events on Multiple Different Surfaces of an Online System
    2.
    发明申请
    Machine Learning Model for Predicting Likelihoods of Events on Multiple Different Surfaces of an Online System 有权

    公开(公告)号：US20250005381A1

    公开(公告)日：2025-01-02

    申请号：US18217356

    申请日：2023-06-30

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Peng Qi ,  Cheng Jia ,  Xiyu Wang ,  Qiao Jiang ,  Sharad Gupta ,  David Pal ,  Joseph Haraldson ,  Zhenbang Chen

    IPC: G06N5/022

    Abstract: An online system manages presentation of content items in various presentation contexts such as when the users are browsing pages or when the users have entered a search query. The online system trains a single unified machine learning model that predicts one or more likelihoods of a target event associated with presentation of a content item in the different presentation contexts. The learned model is applied to a set of candidate content items associated with a presentation opportunity in a specific context. Features that are inapplicable to the specific context may be masked when applying the model. The online system may select between the candidate content items based on the predicted likelihoods using the model trained across the multiple different contexts, such that the prediction for one context may be based in part on learned outcomes in other related contexts.

    INCREMENTALLY UPDATING EMBEDDINGS FOR USE IN A MACHINE LEARNING MODEL BY ACCOUNTING FOR EFFECTS OF THE UPDATED EMBEDDINGS ON THE MACHINE LEARNING MODEL
    3.
    发明公开
    INCREMENTALLY UPDATING EMBEDDINGS FOR USE IN A MACHINE LEARNING MODEL BY ACCOUNTING FOR EFFECTS OF THE UPDATED EMBEDDINGS ON THE MACHINE LEARNING MODEL 审中-公开

    公开(公告)号：US20240362657A1

    公开(公告)日：2024-10-31

    申请号：US18767909

    申请日：2024-07-09

    Applicant: Maplebear Inc.

    Inventor： Chuanwei Ruan ,  Ramasubramanian Balasubramanian ,  Peng Qi

    IPC: G06Q30/0202 ,  G06Q10/087

    CPC classification number: G06Q30/0202 ,  G06Q10/087

    Abstract: An online concierge system uses a model to predict a user's interaction with an item, based on a user embedding for the user and an item embedding for the item. For the model to account for more recent interactions by users with items without retraining the model, the online concierge system generates updated item embeddings and updated user embeddings that account for the recent interactions by users with items. The online concierge system compares performance of the model using the updated item embeddings and the updated user embeddings relative to performance of the model using the existing item embeddings and user embeddings. If the performance of the model decreases, the online concierge system adjusts the updated user embeddings and the updated item embeddings based on the change in performance of the model. The adjusted updated user embeddings and adjusted updated item embeddings are stored for use by the model.

    USING TRANSFER LEARNING TO REDUCE DISCREPANCY BETWEEN TRAINING AND INFERENCE FOR A MACHINE LEARNING MODEL
    5.
    发明公开
    USING TRANSFER LEARNING TO REDUCE DISCREPANCY BETWEEN TRAINING AND INFERENCE FOR A MACHINE LEARNING MODEL 审中-公开

    公开(公告)号：US20230162038A1

    公开(公告)日：2023-05-25

    申请号：US17534184

    申请日：2021-11-23

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Peng Qi ,  Zhenbang Chen

    IPC: G06N3/08 ,  G06N3/04 ,  G06Q30/02

    CPC classification number: G06N3/084 ,  G06N3/04 ,  G06Q30/0202

    Abstract: An online system uses a trained model predicting likelihoods of a user performing a specific interaction with items to order or to rank items for display to the user. The online system trains the model using interactions by users with items displayed by the online system. However, selection, popularity, and position from display of the items affects the model during training. To improve the model, the online system further trains the model using additional training data obtained from displaying items to users in different orders. The further training is done on a limited portion of the model, such as a limited number of layers of the model, to improve the model performance while reducing an amount of additional data to acquire to further train the model.

    TRAINING A MODEL TO PREDICT LIKELIHOODS OF USERS PERFORMING AN ACTION AFTER BEING PRESENTED WITH A CONTENT ITEM
    10.
    发明申请
    TRAINING A MODEL TO PREDICT LIKELIHOODS OF USERS PERFORMING AN ACTION AFTER BEING PRESENTED WITH A CONTENT ITEM 有权

    公开(公告)号：US20220398605A1

    公开(公告)日：2022-12-15

    申请号：US17343026

    申请日：2021-06-09

    Applicant: Maplebear, Inc.(dba Instacart)

    Inventor： Changyao Chen ,  Peng Qi ,  Weian Sheng

    IPC: G06Q30/02 ,  G06N3/04 ,  G06N3/08

    Abstract: An online concierge system trains a user interaction model to predict a probability of a user performing an interaction after one or more content items are displayed to the user. This provides a measure of an effect of displaying content items to the user on the user performing one or more interactions. The user interaction model is trained from displaying content items to certain users of the online concierge system and withholding display of the content items to other users of the online concierge system. To train the user interaction model, the user interaction model is applied to labeled examples identifying a user and value based on interactions the user performed after one or more content items were displayed to the user and interactions the user performed when one or more content items were not used.

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