LANGUAGE MODEL DECODING FOR SEARCH QUERY COMPLETION

    公开(公告)号:US20250156451A1

    公开(公告)日:2025-05-15

    申请号:US18510565

    申请日:2023-11-15

    Applicant: Maplebear Inc.

    Abstract: A language model is used to generate autosuggestions to complete or revise a user's partial search query. An initial partial query is applied to the language model to generate query candidates for completing the search query. The language model may generate the query candidates as additional or alternate tokens for the partial search query. When the user revises the partial query, the previously-generated candidates can be re-used to reduce subsequent processing time for generating additional candidates. The previously-generated candidates are compared with the revised partial query to select which of the candidates to be re-used and expanded for generating additional tokens. Additional tokens can be generated in parallel for the previously-generated candidates or with model values from the previous generation, enabling the tokens to be generated effectively with reduced latency consistent with user expectations for search-related autosuggestions.

    PERSONALIZED RECOMMENDATION OF RECIPES INCLUDING ITEMS OFFERED BY AN ONLINE CONCIERGE SYSTEM BASED ON EMBEDDINGS FOR A USER AND FOR STORED RECIPES

    公开(公告)号:US20220358562A1

    公开(公告)日:2022-11-10

    申请号:US17682444

    申请日:2022-02-28

    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.

    CONTEXT MODELING FOR AN ONLINE CONCIERGE SYSTEM

    公开(公告)号:US20230117762A1

    公开(公告)日:2023-04-20

    申请号:US17503245

    申请日:2021-10-15

    Abstract: An online concierge system improves on methods for presenting content to users. The online concierge system generates a user embedding for a user and recipe embeddings for candidate recipes. The online concierge system generates a context embedding by applying a context embedding model to context data received from a user mobile application. The online concierge system calculates an overall score for each candidate recipe based on a user score and a context score. The user score is calculated based on the user embedding and a recipe embedding for the candidate recipe. The context score is calculated based on the generated context embedding and the recipe embedding for the candidate recipe. The online system selects a recipe for presentation to the user based on the overall scores. The online concierge system trains the context embedding model using a loss function that is based on the user score and the context score.

    Method and medium for personalized recommendation of recipes including based on embeddings for a user and for stored recipes

    公开(公告)号:US11354722B1

    公开(公告)日:2022-06-07

    申请号:US17316250

    申请日:2021-05-10

    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.

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