USING UNSUPERVISED CLUSTERING AND LANGUAGE MODEL TO NORMALIZE ATTRIBUTE TUPLES OF ITEMS IN A DATABASE

    公开(公告)号:US20250005279A1

    公开(公告)日:2025-01-02

    申请号:US18215505

    申请日:2023-06-28

    Abstract: A computer system uses clustering and a large language model (LLM) to normalize attribute tuples for items stored in a database of an online system. The online system collects attribute tuples, each attribute tuple comprising an attribute type and an attribute value for an item. The online system initially clusters the attribute tuples into a first plurality of clusters. The online system generates prompts for input into the LLM, each prompt including a subset of attribute tuples grouped into a respective cluster of the first plurality. Based on the prompts, the LLM generates a second plurality of clusters, each cluster including one or more attribute tuples that have a common attribute type and a common attribute value. The online system maps each attribute tuple to a respective normalized attribute tuple associated with each cluster. The online system rewrites each attribute tuple in the database to a corresponding normalized attribute tuple.

    AUTOMATICALLY GENERATING BASKETS OF ITEMS TO BE RECOMMENDED TO USERS OF AN ONLINE SYSTEM

    公开(公告)号:US20240394771A1

    公开(公告)日:2024-11-28

    申请号:US18202768

    申请日:2023-05-26

    Abstract: Embodiments relate to automatically generating a basket of items to be recommended to a user of an online system. The online system communicates a basket opportunity to a group of retailers, wherein the basket opportunity defines a plurality of item categories each associated with a respective item to be included in a basket. The online system receives, from each retailer in response to the basket opportunity, a respective bid of a plurality of bids for the basket opportunity. The online system applies a computer model to each bid to determine a score for each bid and selects a winning bid for the user based on determined scores for the bids. For each item category, the online system populates the basket with a respective item from a catalog of a retailer that is associated with the winning bid. The online system then presents the basket with items to the user.

    GENERATING SUGGESTED INSTRUCTIONS THROUGH NATURAL LANGUAGE PROCESSING OF INSTRUCTION EXAMPLES

    公开(公告)号:US20240070393A1

    公开(公告)日:2024-02-29

    申请号:US17821889

    申请日:2022-08-24

    CPC classification number: G06F40/284 G06Q30/0621

    Abstract: An online concierge system generates suggested instructions for presentation to a user. The online concierge system access instruction examples corresponding to a target item category and generates candidate instruction representations based on instruction messages within each instruction example. The online concierge system generates preliminary scores for the candidate instruction representations that are directly related to an intra-category frequency of use of the instruction tokens of the candidate instruction representation within the target item category. The online system normalizes these preliminary scores for the candidate instruction representations based on the inter-category frequency of use of the instruction tokens in all item categories to generate final scores for the candidate instruction representations. The online concierge system selects a set of instruction representations based on these final scores and generates suggested instructions based on the set of instruction representations.

    INFERRING CATEGORIES IN A PRODUCT TAXONOMY USING A REPLACEMENT MODEL

    公开(公告)号:US20220292567A1

    公开(公告)日:2022-09-15

    申请号:US17196855

    申请日:2021-03-09

    Abstract: An online concierge system accesses a hierarchical taxonomy of products each labeled with a category of the hierarchical taxonomy. The online concierge system receives, from an inventory database, an unlabeled product, which not included in the hierarchical taxonomy. The online concierge system inputs the unlabeled product to a replacement model. The replacement model is trained to output, for each of one or more labeled products from the hierarchical taxonomy, a likelihood that a user would select the labeled product as a replacement for an input product. The online concierge system selects a labeled product from the one or more labeled products based on the likelihoods. The online concierge system adds the unlabeled product to a category of the hierarchical taxonomy based on the selected labeled product.

    DETERMINING RECOMMENDED SEARCH TERMS FOR A USER OF AN ONLINE CONCIERGE SYSTEM

    公开(公告)号:US20230139335A1

    公开(公告)日:2023-05-04

    申请号:US18090506

    申请日:2022-12-29

    Abstract: An online concierge system may determine recommended search terms for a user. The online concierge system may receive a request from a user to view a user interface configured to receive a search query. The online concierge system retrieves long-term activity data including previous search terms entered by the user while searching for items to add to an online shopping cart. For each previous search term, the online concierge system retrieves categorical search terms corresponding to one or more categories to which the previous search term was mapped. The online concierge system determines a set of nearby categorical search terms and sends, for display via a client device, the set of nearby categorical search terms as recommended search terms.

    DETERMINING RECOMMENDED SEARCH TERMS FOR A USER OF AN ONLINE CONCIERGE SYSTEM

    公开(公告)号:US20210287271A1

    公开(公告)日:2021-09-16

    申请号:US16815846

    申请日:2020-03-11

    Abstract: An online concierge system may determine recommended search terms for a user. The online concierge system may receive a request from a user to view a user interface configured to receive a search query. The online concierge system retrieves long-term activity data including previous search terms entered by the user while searching for items to add to an online shopping cart. For each previous search term, the online concierge system retrieves categorical search terms corresponding to one or more categories to which the previous search term was mapped. The online concierge system determines a set of nearby categorical search terms and sends, for display via a client device, the set of nearby categorical search terms as recommended search terms.

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