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.

    DETERMINING SEARCH RESULTS FOR AN ONLINE SHOPPING CONCIERGE PLATFORM

    公开(公告)号:US20240177212A1

    公开(公告)日:2024-05-30

    申请号:US18072353

    申请日:2022-11-30

    CPC classification number: G06Q30/0631

    Abstract: To determine search results for an online shopping concierge platform, the platform may receive, from a computing device associated with a customer of an online shopping concierge platform, data describing one or more search parameters input by the customer; identify, based at least in part on the data describing the search parameter(s), products offered by the online shopping concierge platform that are at least in part responsive to the search parameter(s); and determine, for each product and based at least in part on one or more machine learning (ML) models, a relevance of the product to one or more taxonomy levels of a product catalog associated with the online shopping concierge platform, a likelihood that the customer would be offended by inclusion of the product amongst displayed responsive search results, and/or the like.

    ATTRIBUTE NODE WIDGETS IN SEARCH RESULTS FROM AN ITEM GRAPH

    公开(公告)号:US20230222162A1

    公开(公告)日:2023-07-13

    申请号:US18185091

    申请日:2023-03-16

    CPC classification number: G06Q30/0201 G06Q30/0641 G06Q30/0635

    Abstract: An online concierge system generates an item graph connecting item nodes with attribute nodes of the items. Example attributes include a brand, a category, a department, or any other suitable information about the item. When the online concierge system receives a search query to identify one or more items from a customer, the online concierge system parses the search query into combinations of terms and identifies item nodes and attribute nodes related to the search query. The online concierge system identifies item nodes and attribute nodes that are likely to result in a conversion. Information about the identified nodes is presented to the customer. The customer may select an item node to purchase the item, or an attribute node to execute a new search query based on terms associated with the attribute node.

    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.

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