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    Machine Learning Model for Click Through Rate Predication Using Three Vector Representations
    1.
    发明公开
    Machine Learning Model for Click Through Rate Predication Using Three Vector Representations 审中-公开

    公开(公告)号：US20240104631A1

    公开(公告)日：2024-03-28

    申请号：US18528744

    申请日：2023-12-04

    Applicant: Maplebear Inc.

    Inventor： Ramasubramanian Balasubramanian ,  Saurav Manchanda

    IPC: G06Q30/0601 ,  G06Q30/0202 ,  G06Q30/0241

    CPC classification number: G06Q30/0631 ,  G06Q30/0202 ,  G06Q30/0241

    Abstract: An online concierge system uses a machine learning click through rate model to select promoted items based on user embeddings, item embeddings, and search query embeddings. Embeddings obtained by an embedding model may be used as inputs to the click through rate model. The embedding model may be trained using different actions to score the strength of a customer interaction with an item. For example, a customer purchasing an item may be a stronger signal than a customer placing an item in a shopping cart, which in turn may be a stronger signal than a customer clicking on an item. The online concierge system generates a ranking of candidate promoted items based on the search query and using the click through rate model. Based on the ranking, the online concierge system displays promoted items along with the organic search results to the customer.

    ATTRIBUTE PREDICTION WITH MASKED LANGUAGE MODEL
    2.
    发明公开
    ATTRIBUTE PREDICTION WITH MASKED LANGUAGE MODEL 审中-公开

    公开(公告)号：US20240005096A1

    公开(公告)日：2024-01-04

    申请号：US17855799

    申请日：2022-07-01

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Ramasubramanian Balasubramanian ,  Saurav Manchanda

    IPC: G06F40/284 ,  G06F40/186 ,  G06N5/02

    CPC classification number: G06F40/284 ,  G06F40/186 ,  G06N5/022

    Abstract: A masked language model is used to predict an attribute of an object, such as a physical item or product based on the predicted value of a masked token. The masked language model may be trained on a general corpus of text for the language, such that the masked language model learns context and text token relationships. Information about the object may then be added to a query template that structures the item information in an attribute query that may be interpretable by the masked language model to provide a resulting token related to the provided information or to confirm or reject an attribute specified in the query template.

    Search Relevance Model Using Self-Adversarial Negative Sampling
    3.
    发明公开
    Search Relevance Model Using Self-Adversarial Negative Sampling 审中-公开

    公开(公告)号：US20230252549A1

    公开(公告)日：2023-08-10

    申请号：US18107854

    申请日：2023-02-09

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Yuqing Xie ,  Taesik Na ,  Saurav Manchanda

    IPC: G06Q30/0601 ,  G06Q30/0201

    CPC classification number: G06Q30/0631 ,  G06Q30/0201

    Abstract: To train an embedding-based model to determine relevance between items and queries, an online system generates training data from previously received queries and interactions with results for the queries. The training data includes positive training examples including a query and an item with which a user performed a specific interaction after providing the query. To generate negative training examples for the query to include in the training data, the online system determines measures of similarity between items with which the specific interaction was not performed and the query. The online system may weight a loss function for the embedding-based model by the measure of similarity for a negative example, increasing the effect of a negative example including a query and an item with a larger measure of similarity. In other embodiments, the online system selects negative training examples based on the measures of similarities between items and queries in pairs.

    CONTENT SELECTION WITH INTER-SESSION REWARDS IN REINFORCEMENT LEARNING
    5.
    发明公开
    CONTENT SELECTION WITH INTER-SESSION REWARDS IN REINFORCEMENT LEARNING 审中-公开

    公开(公告)号：US20240330695A1

    公开(公告)日：2024-10-03

    申请号：US18129023

    申请日：2023-03-30

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Saurav Manchanda ,  Ramasubramanian Balasubramanian

    IPC: G06N3/092 ,  G06N3/04

    CPC classification number: G06N3/092 ,  G06N3/04

    Abstract: A reinforcement learning model selects a content composition based, in part, on inter-session rewards. In addition to near-in-time rewards of user interactions with a content composition for evaluating possible actions, the reinforcement learning model also generates a reward and/or penalty based on between-session information, such as the time between sessions. This permits the reinforcement learning model to learn to evaluate content compositions not only on the immediate user response, but also on the effect of future user engagement. To determine a composition for a search query, the reinforcement learning model generates a state representation of the user and search query and evaluates candidate content compositions based on learned parameters of the reinforcement learning model that evaluates inter-session rewards of the content compositions.

    ITEM ATTRIBUTE DETERMINATION USING A CO-ENGAGEMENT GRAPH
    6.
    发明公开
    ITEM ATTRIBUTE DETERMINATION USING A CO-ENGAGEMENT GRAPH 审中-公开

    公开(公告)号：US20240104632A1

    公开(公告)日：2024-03-28

    申请号：US17935916

    申请日：2022-09-27

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Creagh Briercliffe ,  Chuan Lei ,  Saurav Manchanda ,  Min Xie

    IPC: G06Q30/06

    CPC classification number: G06Q30/0635 ,  G06Q30/0613 ,  G06Q30/0627 ,  G06Q30/0639

    Abstract: An online concierge system uses a co-engagement graph to assign attribute values to items for which those attribute values are uncertain. A co-engagement graph is a graph with nodes that represent items and edges that represent co-engagement between items. The online concierge system generates a co-engagement graph for a set of items based on item engagement data and item data for the items. The set of items includes items for which the online concierge system has an attribute value for a target attribute and items for which the online concierge system does not have an attribute value for the target attribute. The online concierge system identifies a node that corresponds to an unknown item and identifies a node connected to that first node that corresponds to a known item. The online concierge system assigns the attribute value for the known item to the unknown item.

    Machine learning model for click through rate prediction using three vector representations
    10.
    发明授权
    Machine learning model for click through rate prediction using three vector representations 有权

    公开(公告)号：US11861677B2

    公开(公告)日：2024-01-02

    申请号：US17513739

    申请日：2021-10-28

    Applicant: Maplebear Inc.

    Inventor： Ramasubramanian Balasubramanian ,  Saurav Manchanda

    IPC: G06Q30/00 ,  G06Q30/0601 ,  G06Q30/0241 ,  G06Q30/0202

    CPC classification number: G06Q30/0631 ,  G06Q30/0202 ,  G06Q30/0241

    Abstract: An online concierge system uses a machine learning click through rate model to select promoted items based on user embeddings, item embeddings, and search query embeddings. Embeddings obtained by an embedding model may be used as inputs to the click through rate model. The embedding model may be trained using different actions to score the strength of a customer interaction with an item. For example, a customer purchasing an item may be a stronger signal than a customer placing an item in a shopping cart, which in turn may be a stronger signal than a customer clicking on an item. The online concierge system generates a ranking of candidate promoted items based on the search query and using the click through rate model. Based on the ranking, the online concierge system displays promoted items along with the organic search results to the customer.

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