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    BOOSTING SCORES FOR RANKING ITEMS MATCHING A SEARCH QUERY
    22.
    发明公开
    BOOSTING SCORES FOR RANKING ITEMS MATCHING A SEARCH QUERY 审中-公开

    公开(公告)号：US20240104622A1

    公开(公告)日：2024-03-28

    申请号：US17955250

    申请日：2022-09-28

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Vinesh Reddy Gudla ,  Tyler Russell Tate ,  Tejaswi Tenneti ,  Akshay Nair

    IPC: G06Q30/06 ,  G06Q30/02

    CPC classification number: G06Q30/0629 ,  G06Q30/0201 ,  G06Q30/0204

    Abstract: An online system receives a search query from a client device associated with a user and queries a database including item data for a set of items matching the query, in which the set of items is at a retailer location associated with a retailer type and each item is associated with an item category. For each item of the set, a machine learning model is applied to predict a probability of conversion for the user and item and a score is computed based on an expected value, in which the expected value is based on a value associated with the item and the probability. The score for each item is boosted based on the item category, retailer type, or a user segment that is based on the user's historical order data. The items are ranked based on the boosted scores and the ranking is sent to the client device.

    PERSONALIZED RECOMMENDATION OF RECIPES INCLUDING ITEMS OFFERED BY AN ONLINE CONCIERGE SYSTEM BASED ON EMBEDDINGS FOR A USER AND FOR STORED RECIPES
    27.
    发明申请
    PERSONALIZED RECOMMENDATION OF RECIPES INCLUDING ITEMS OFFERED BY AN ONLINE CONCIERGE SYSTEM BASED ON EMBEDDINGS FOR A USER AND FOR STORED RECIPES 有权

    公开(公告)号：US20220358562A1

    公开(公告)日：2022-11-10

    申请号：US17682444

    申请日：2022-02-28

    Applicant: Maplebear Inc.(dba Instacart)

    Inventor： Manmeet Singh ,  Tyler Russell Tate ,  Tejaswi Tenneti ,  Sharath Rao Karikurve

    IPC: G06Q30/06

    Abstract: An online concierge shopping system identifies recipes to users to encourage them to include items from the recipes in orders. The online concierge system maintains user embeddings for users and recipe embeddings for recipes. For users who have not placed orders, recipes are recommended based on global user interactions with recipes. Users who have previously ordered items from recipes are suggested recipes selected based on a similarity of their user embedding to recipe embeddings. Users who have purchased items but not from recipes are compared to a set of similar users based on the user embeddings, and recipes with which users of the set of similar users interacted are used for identifying recipes to the users. A recipe graph may be maintained by the online concierge system to identify similarities between recipes for expanding candidate recipes to suggest to users.

    FALSE NEGATIVE PREDICTION FOR TRAINING A MACHINE-LEARNING MODEL
    28.
    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.

    CONTEXTUAL BANDIT MODEL FOR QUERY PROCESSING MODEL SELECTION
    29.
    发明申请
    CONTEXTUAL BANDIT MODEL FOR QUERY PROCESSING MODEL SELECTION 有权

    公开(公告)号：US20250139176A1

    公开(公告)日：2025-05-01

    申请号：US18496724

    申请日：2023-10-27

    Applicant: Maplebear Inc.

    Inventor： Vinesh Reddy Gudla ,  David Vengerov ,  Tejaswi Tenneti

    IPC: G06F16/9532 ,  G06Q30/0201 ,  G06Q30/0282 ,  G06Q30/0601

    Abstract: A system uses a contextual bandit model for query processing. The system receives, from a client device, a user query for identifying one or more items by the system. The user query is described by one or more query features. The system obtains one or more contextual features describing a context of the user query. The system applies a contextual bandit model to the query features and the contextual features to select a query processing model from a plurality of query processing models. The system applies the selected query processing model to the user query to obtain query results. The system transmits the query results for display on the client device.

    RANKING SEARCH RESULTS BASED ON APPEASEMENT SIGNALS AND QUERY SPECIFICITY
    30.
    发明申请
    RANKING SEARCH RESULTS BASED ON APPEASEMENT SIGNALS AND QUERY SPECIFICITY 有权

    公开(公告)号：US20250077529A1

    公开(公告)日：2025-03-06

    申请号：US18241093

    申请日：2023-08-31

    Applicant: Maplebear Inc.

    Inventor： Levi Boxell ,  Vinesh Reddy Gudla ,  Michael Kurish ,  Raochuan Fan ,  Tilman Drerup ,  Tejaswi Tenneti

    IPC: G06F16/2457 ,  G06F16/248 ,  G06N20/00

    Abstract: An online system displays items to a user in search results based on appeasement scores for the items, adjusted according to how specific the search query is. The online system receives a search query from a user of an online system. The online system computes a query specificity score, a measure of the specificity of the search query. The online system accesses candidate items from a database that potentially match the search query. For each candidate item, the online system may compute or predict an appeasement score. The online system adjusts the appeasement score based on the query specificity score such that a more specific query weights the appeasement score lower than a less specific query. The online system may then compute a ranking score based on the adjusted appeasement score and display the candidate items to the user based on their ranking scores.

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