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    Providing and displaying search results in response to a query
    11.
    发明授权
    Providing and displaying search results in response to a query 有权

    公开(公告)号：US12266006B2

    公开(公告)日：2025-04-01

    申请号：US18159357

    申请日：2023-01-25

    Applicant: Maplebear Inc.

    Inventor： Taesik Na ,  Vinesh Reddy Gudla ,  Xiao Xiao

    IPC: G06Q30/06 ,  G06F16/9535 ,  G06Q30/0201 ,  G06Q30/0601

    Abstract: An online system displays search results in response to a query by receiving a query from a customer. An online system accesses a set of candidate items and computes a relevance score and personalization score for each item. The online system computes the relevance score based on query data and item data and may normalize the relevance score. The online system computes the personalization score based on item data, such as an item embedding, and user data, such as a user embedding. The online system computes a query specificity score and adjusts the personalization score with the query specificity score such that generic queries have high personalization scores and specific queries have low personalization scores. The online system combines the relevance and personalization scores for each candidate item into a ranking score and displays the candidate items to the customer based on their ranking scores.

    SUGGESTING KEYWORDS TO DEFINE AN AUDIENCE FOR A RECOMMENDATION ABOUT A CONTENT ITEM
    13.
    发明公开
    SUGGESTING KEYWORDS TO DEFINE AN AUDIENCE FOR A RECOMMENDATION ABOUT A CONTENT ITEM 审中-公开

    公开(公告)号：US20240070210A1

    公开(公告)日：2024-02-29

    申请号：US17899441

    申请日：2022-08-30

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Ramasubramanian Balasubramanian ,  Taesik Na ,  Karuna Ahuja

    IPC: G06F16/9532 ,  G06Q30/06

    CPC classification number: G06F16/9532 ,  G06Q30/0631

    Abstract: A computer-implemented method for suggesting keywords as a search term of a content item includes receiving, from a content provider, information about the content item in a database of content items. The method further includes generating a set of seed keywords related to the content item, and expanding the set of seed keywords to a plurality of candidate keywords. The plurality of candidate keywords are then scored based, at least in part, on an engagement metric measuring a user engagement with the content item in response to being presented with results from a search query comprising the candidate keyword. A candidate keyword is then selected from the plurality of candidate keywords based on the scoring, and stored relationally to the content item to define an audience for a recommendation about the content item, providing a suggestion to the content provider.

    MODIFYING RANKINGS OF ITEMS IN SEARCH RESULTS BASED ON ITEM AVAILABILITIES AND SEARCH QUERY ATTRIBUTES
    15.
    发明申请
    MODIFYING RANKINGS OF ITEMS IN SEARCH RESULTS BASED ON ITEM AVAILABILITIES AND SEARCH QUERY ATTRIBUTES 有权

    公开(公告)号：US20250005654A1

    公开(公告)日：2025-01-02

    申请号：US18217329

    申请日：2023-06-30

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Raochuan Fan ,  Prakash Putta ,  Vinesh Reddy Gudla ,  Nkemakonam Paulet Okoye ,  Taesik Na ,  Tejaswi Tenneti

    IPC: G06Q30/0601

    Abstract: An online concierge system allows a customer to search items offered by a retailer by providing a set of items to the customer based on a search query. To account for varying availability of items at the retailer, the online concierge system modifies rankings in the set of items having less than a threshold predicted availability at the retailer. This reduces a likelihood selection of an item likely to be unavailable at the retailer. To maintain customer confidence in the items selected based on the search results by maintaining visibility of items relevant to the search query, the online concierge system determines how much an item is modified within the set based on search query attributes, item attributes, or customer characteristics. This allows different items to be adjusted different amounts in a set based on the item, as well as the search query for which the item was selected.

    MACHINE LEARNED MODELS FOR SEARCH AND RECOMMENDATIONS
    17.
    发明公开
    MACHINE LEARNED MODELS FOR SEARCH AND RECOMMENDATIONS 审中-公开

    公开(公告)号：US20240241897A1

    公开(公告)日：2024-07-18

    申请号：US18415551

    申请日：2024-01-17

    Applicant: Maplebear Inc.

    Inventor： Haixun Wang ,  Taesik Na ,  Li Tan ,  Jian Li ,  Xiao Xiao

    IPC: G06F16/33 ,  G06F16/338 ,  G06N20/00

    CPC classification number: G06F16/3344 ,  G06F16/338 ,  G06N20/00

    Abstract: A system may generate a prompt based in part on a search query from a customer client device. The prompt instructs a machine learned model to provide item predictions. And the model was trained by: converting structured data describing items of an online catalog to annotated text data (unstructured data), generating training examples based in part on the annotated text data, and training the model using the training examples. The system may receive item predictions generated by the prompt being applied to the machine learned model, the item predictions may have corresponding item identifiers. The item predictions are processed to identify a recommended item from the item predictions. The processing includes determining item information for the recommended item using an item identifier associated with the recommended item. The item information is provided to the customer client device.

    FALSE NEGATIVE PREDICTION FOR TRAINING A MACHINE-LEARNING MODEL
    20.
    发明申请
    FALSE NEGATIVE PREDICTION FOR TRAINING A MACHINE-LEARNING MODEL 有权

    公开(公告)号：US20250147997A1

    公开(公告)日：2025-05-08

    申请号：US18932301

    申请日：2024-10-30

    Applicant: Maplebear Inc.

    Inventor： Xiaochen Wang ,  Taesik Na ,  Xiao Xiao ,  Ruhan Zhang ,  Xuan Zhang ,  Tejaswi Tenneti ,  Haixun Wang

    IPC: G06F16/35 ,  G06F16/383

    Abstract: An online system updates the labels on negative examples to account for the possibility that the example is a false negative. The system generates a set of initial training examples that each include a query input by the user and item data for an item presented as a result to the user's query. Each training example also includes an initial label, which represents whether the user interacted with the item presented as a search result. The online system updates the initial label for a negative training example by identifying a set of bridge queries and computing a similarity score between the query for the training example and the bridge queries. The online system computes an updated label for the negative example based on the similarity scores and updates the training example with the updated label.

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