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    System and method for personalized item recommendations through large-scale deep-embedding architecture
    3.
    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
    4.
    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.

    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：US20210241344A1

    公开(公告)日：2021-08-05

    申请号：US16777571

    申请日：2020-01-30

    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 ,  G06F16/903

    摘要： A method including 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 can 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 generating an approximate nearest neighbor index for the two sets of item embeddings. The method additionally can include receiving a basket including basket items selected by a user from the item catalog. The method further 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 lookup call to the approximate nearest neighbor index using a query vector associated with the user and the respective anchor item. 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.

    PERSONALIZED ITEM RECOMMENDATIONS THROUGH LARGE-SCALE DEEP-EMBEDDING ARCHITECTURE
    6.
    发明申请
    PERSONALIZED ITEM RECOMMENDATIONS THROUGH LARGE-SCALE DEEP-EMBEDDING ARCHITECTURE 有权

    公开(公告)号：US20210241343A1

    公开(公告)日：2021-08-05

    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： G06Q30/06 ,  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.

    System, method, and computer readable medium for automatic item rankings
    8.
    发明授权
    System, method, and computer readable medium for automatic item rankings 有权

    公开(公告)号：US11610249B2

    公开(公告)日：2023-03-21

    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： G06Q30/00 ,  G06Q30/0601 ,  G06Q10/087 ,  G06F16/2457

    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.

    Systems and methods for prediction of item quantity
    9.
    发明授权
    Systems and methods for prediction of item quantity 有权

    公开(公告)号：US11544763B2

    公开(公告)日：2023-01-03

    申请号：US16778905

    申请日：2020-01-31

    申请人： Walmart Apollo, LLC

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

    IPC分类号： G06Q30/00 ,  G06Q30/06 ,  G06F3/0482

    摘要： Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving a user identifier, receiving an item identifier, determining user item quantity information related to quantities of the item previously selected by the user, determining a respective household size for each user, and determining aggregate household item quantity information related to quantities of the item previously selected by an aggregate of users of the same household size. If a first threshold level of the quantity of transactions is met, a recommended quantity is based on the user item quantity information, and if not, the recommended quantity is based on the aggregate household item quantity information. The user interface of the electronic device is updated to notify the user of the recommended quantity. Other embodiments are disclosed herein.

    SYSTEMS AND METHODS FOR GENERATING REAL-TIME RECOMMENDATIONS
    10.
    发明申请
    SYSTEMS AND METHODS FOR GENERATING REAL-TIME RECOMMENDATIONS 有权

    公开(公告)号：US20220222706A1

    公开(公告)日：2022-07-14

    申请号：US17147980

    申请日：2021-01-13

    申请人： Walmart Apollo, LLC

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

    IPC分类号： G06Q30/02 ,  G06Q30/06

    摘要： This application relates to apparatus and methods for providing recommended items to advertise. In some examples, a computing device determines a first set of items for recommendation based on historical user data associated with a user, and a second set of items for recommendation based on real-time user session data for the user. The computing device may then determine a subset of the first set of items based on associated scores and a predetermined threshold number of first items that can be presented for optimal user interaction. The computing device may generate a set of item recommendations by combining the subset of the first set of items and at least one of the second set of items to present to the user as advertisements.