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    METHODS AND APPARATUS FOR AUTOMATIC ITEM RANKINGS
    1.
    发明申请
    METHODS AND APPARATUS FOR AUTOMATIC ITEM RANKINGS 有权

    公开(公告)号：US20220222729A1

    公开(公告)日：2022-07-14

    申请号：US17147895

    申请日：2021-01-13

    申请人： Walmart Apollo, LLC

    发明人： Rahul Iyer ,  Shashank Kedia ,  Anirudha Sundaresan ,  Shubham Gupta ,  Praveenkumar Kanumala ,  Stephen Dean Guo ,  Kannan Achan

    IPC分类号： G06Q30/06 ,  G06F16/2457 ,  G06Q10/08

    摘要： This application relates to apparatus and methods for automatically determining item relevancy based on textual information. In some examples, a computing device receives a search query, and a plurality of items corresponding to the search query. The computing device may identify one or more features of the search query. The computing device may generate relevancy values for each of the items based on the features of the search query, and features of each of the plurality of items. For example, the computing device may generate, for each of the items, a plurality of relevance values, each relevance value generated based on a feature of the search query and corresponding features of the item. The computing device may transmit the generated relevancy values for the plurality of items. In some examples, the computing device may rank the plurality of items based on the generated relevancy values.

    PERSONALIZED ITEM RECOMMENDATIONS THROUGH LARGE-SCALE DEEP-EMBEDDING ARCHITECTURE WITH REAL-TIME INFERENCING
    5.
    发明申请
    PERSONALIZED ITEM RECOMMENDATIONS THROUGH LARGE-SCALE DEEP-EMBEDDING ARCHITECTURE WITH REAL-TIME INFERENCING 有权

    公开(公告)号：US20210398192A1

    公开(公告)日：2021-12-23

    申请号：US17466277

    申请日：2021-09-03

    申请人： Walmart Apollo, LLC

    发明人： Aditya Mantha ,  Yokila Arora ,  Shubham Gupta ,  Praveenkumar Kanumala ,  Stephen Dean Guo ,  Kannan Achan

    IPC分类号： G06Q30/06 ,  G06F16/9035

    摘要： A method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media. The method can include training two sets of item embeddings for items in an item catalog and a set of user embeddings for users, using a triple embeddings model, with triplets. The triplets each include a respective first user of the users, a respective first item from the item catalog, and a respective second item from the item catalog, in which the respective first user selected the respective first item and the respective second item in a respective same basket. The method also can include randomly sampling an anchor item from a category of items selected by a user. The method additionally can include generating a list of complementary items using a query vector associated with the user and the anchor item. The query vector is generated for the user and the anchor item using the two sets of item embeddings and the set of user embeddings. Other embodiments are disclosed.

    AUTOMATICALLY DETERMINING IN REAL-TIME A TRIGGERING MODEL FOR PERSONALIZED RECOMMENDATIONS
    6.
    发明申请
    AUTOMATICALLY DETERMINING IN REAL-TIME A TRIGGERING MODEL FOR PERSONALIZED RECOMMENDATIONS 有权

    公开(公告)号：US20210374832A1

    公开(公告)日：2021-12-02

    申请号：US17399977

    申请日：2021-08-11

    申请人： Walmart Apollo, LLC

    发明人： Aditya Mantha ,  Rahul Radhakrishnan Iyer ,  Shashank Kedia ,  Shubham Gupta ,  Praveenkumar Kanumala ,  Stephen Dean Guo ,  Kannan Achan

    IPC分类号： G06Q30/06 ,  G06N7/00

    摘要： A method including building a recommendation triggering model. The method can include receiving, via a user device of a user through a network, an add-to-cart command associated with an anchor item for the user. The method further can include determining, in real-time after receiving the add-to-cart command, a recommendation for one or more complementary items of the anchor item for the user. The method also can include determining, in real-time after determining the recommendation, a recommendation confidence for the recommendation. The method additionally can include after determining the recommendation confidence, when the recommendation confidence is positive, transmitting, in real-time through the network, the one or more complementary items to be presented to the user via the user device. The method likewise can include after determining the recommendation confidence, when the recommendation confidence is not positive, refraining from transmitting the one or more complementary items to the user. Other embodiments are disclosed.

    System and method for personalized item recommendations through large-scale deep-embedding architecture
    9.
    发明授权
    System and method for personalized item recommendations through large-scale deep-embedding architecture 有权

    公开(公告)号：US11288730B2

    公开(公告)日：2022-03-29

    申请号：US16777555

    申请日：2020-01-30

    申请人： Walmart Apollo, LLC

    发明人： Yokila Arora ,  Aditya Mantha ,  Shubham Gupta ,  Praveenkumar Kanumala ,  Stephen Dean Guo ,  Kannan Achan

    IPC分类号： G06Q30/00 ,  G06Q30/06 ,  G06N20/00

    摘要： A method including receiving a basket including basket items selected by a user from an item catalog. The method also can include grouping the basket items of the basket into categories based on a respective item category of each of the basket items. The method additionally can include randomly sampling a respective anchor item from each of the categories. The method further can include generating a respective list of complementary items for the respective anchor item for the each of the categories based on a respective score for each of the complementary items generated using two sets of trained item embeddings for items in the item catalog and using trained user embeddings for the user. The two sets of trained item embeddings and the trained user embeddings can be trained using a triple embeddings model with triplets. The triplets each can include a respective first user of users, a respective first item from the item catalog, and a respective second item from the item catalog, in which the respective first user selected the respective first item and the respective second item in a respective same basket. The method additionally can include building a list of personalized recommended items for the user based on the respective lists of the complementary items for the categories. The method further can include sending instructions to display, to the user on a user interface of a user device, at least a portion of the list of personalized recommended items. Other embodiments are disclosed.

    AUTOMATICALLY DETERMINING IN REAL-TIME A TRIGGERING MODEL FOR PERSONALIZED RECOMMENDATIONS
    10.
    发明申请
    AUTOMATICALLY DETERMINING IN REAL-TIME A TRIGGERING MODEL FOR PERSONALIZED RECOMMENDATIONS 有权

    公开(公告)号：US20210241349A1

    公开(公告)日：2021-08-05

    申请号：US16779541

    申请日：2020-01-31

    申请人： Walmart Apollo, LLC

    发明人： Aditya Mantha ,  Rahul Radhakrishnan Iyer ,  Shashank Kedia ,  Shubham Gupta ,  Praveenkumar Kanumala ,  Stephen Dean Guo ,  Kannan Achan

    IPC分类号： G06Q30/06 ,  G06N7/00

    摘要： A method including building a recommendation triggering model. The method can include receiving, via a user device of a user through a network, an add-to-cart command associated with an anchor item in a session by the user. The method further can include determining, in real-time after receiving the add-to-cart command, a recommendation for one or more complementary items based at least in part on: (a) the anchor item; and (b) a user profile of the user. The method also can include determining, in real-time after determining the recommendation, a recommendation confidence for the recommendation based at least in part on one or more of: (a) the user profile; (b) the anchor item; (c) the one or more complementary items; or (d) one or more feedbacks from the user associated with one or more prior recommendations in the session. The method additionally can include after determining the recommendation confidence, when the recommendation confidence is positive, transmitting, in real-time through the network, the one or more complementary items to be presented to the user via the user device. The method likewise can include after determining the recommendation confidence, when the recommendation confidence is not positive, refraining from transmitting the one or more complementary items to the user. Other embodiments are disclosed.