SYSTEM AND METHOD FOR DETERMINING CROSS-POLLINATION PRODUCT RECOMMENDATIONS

    公开(公告)号:US20240257216A1

    公开(公告)日:2024-08-01

    申请号:US18427118

    申请日:2024-01-30

    IPC分类号: G06Q30/0601 G06Q30/0201

    摘要: A computer-implemented method including determining an anchor product type for an anchor item. The method further can include determining at least one associated product type for the anchor product type. Determining the at least one associated product type for the anchor product type further can include: (a) determining at least one complementary product type for the anchor product type; (b) determining an anchor-product-type-name vector for an anchor-product-type name of the anchor product type; (c) determining a respective complementary-product-type-name vector for a respective complementary-product-type name of each of the at least one complementary product type; (d) determining a respective product-type-name similarity score between the anchor-product-type-name vector and the respective complementary-product-type-name vector for each of the at least one complementary product type; and (e) determining the at least one associated product type based at least in part on a product-type-level threshold and the respective product-type-name similarity score for each of the at least one complementary product type. The method also can include determining at least one associated item for the anchor item based at least in part on the at least one associated product type and at least one recommended item for the anchor item. The method further can include transmitting, via a computer network, the at least one associated item to be displayed on a user interface for a user. Other embodiments are described.

    SYSTEMS AND METHODS FOR ANALYZING AND DISPLAYING PRODUCTS

    公开(公告)号:US20240257211A1

    公开(公告)日:2024-08-01

    申请号:US18103236

    申请日:2023-01-30

    IPC分类号: G06Q30/0601 G06Q30/0202

    摘要: Systems and methods including one or more processors and one or more non-transitory computer readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: receiving historical marketplace information for a user in a marketplace corresponding to products previously purchased by the user; processing the products to group the products into one or more product-type clusters; analyzing the one or more product-type clusters to determine respective inter-purchase interval (IPI) likelihood scores for each product in each of the one or more product-type clusters; identifying one or more candidate products from the one or more product-type clusters that have respective IPI likelihood scores that satisfy one or more thresholds; determining a respective time and a respective duration for a respective re-purchase notification for the user based on the respective IPI likelihood score for each of the one or more candidate products; ranking the one or more candidate products based on the respective IPI likelihood scores for the one or more candidate products; and transmitting a re-purchase notification to the user via a graphical user interface (GUI) that includes at least a subset of the one or more candidate products, the GUI including a first section that includes a first portion of the at least the subset of the one or more candidate products and a second section that includes a second portion of the at least the subset of the one or more candidate products. Other embodiments are disclosed.

    SYSTEMS AND METHODS FOR COLD-START RECOMMENDATION USING LARGESCALE GRAPH MODELS

    公开(公告)号:US20240256578A1

    公开(公告)日:2024-08-01

    申请号:US18104184

    申请日:2023-01-31

    摘要: Systems and methods of generating interfaces including recommended items selected by a graph-based cold-start (GCS) model are disclosed. A request for an interface is received and a set of interface items is generated for inclusion in the interface. The set of interface items is selected, at least in part, by a GCS model including a semantic similarity component and a viewed-also-viewed component. The set of interface items is generated based on a combination of an output of the semantic similarity component and an output of the viewed-also-viewed component. The interface including the set of interface items is generated and transmitted to a system that generated the request for the interface.

    MULTI-PATH COMPLIMENTARY ITEMS RECOMMENDATIONS

    公开(公告)号:US20240249340A1

    公开(公告)日:2024-07-25

    申请号:US18623691

    申请日:2024-04-01

    摘要: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: generating, using a trained machine-learning model, personalized product-type metrics for a user based on historic activity of the user and product-type pairs in an item taxonomy; determining top product types for the user based on an anchor item; determining a set of first items associated with the top product types; ranking each item in the set of first items for (i) the anchor item and (ii) for each item in the set of first items; and selecting, based on the ranking, a set of top items from the set of first items to be personalized complementary item recommendations for the user based on the anchor item. Other embodiments are described.

    SYSTEMS AND METHODS FOR SEQUENTIAL MODEL FRAMEWORK FOR NEXT-BEST USER STATE

    公开(公告)号:US20240220286A1

    公开(公告)日:2024-07-04

    申请号:US18148254

    申请日:2022-12-29

    IPC分类号: G06F9/451 G06N3/08

    CPC分类号: G06F9/451 G06N3/08

    摘要: Systems and methods of generating an interface including elements related to a next best state prediction are disclosed. A request for an interface including a user identifier is received. A next state prediction engine receives a sequence unit set including at least one sequence unit associated with the user identifier and a set of features associated with the at least one sequence unit and generates at least one next state prediction using a trained sequential prediction model. The trained sequential prediction model is configured to receive the sequence unit set and the set of features for the at least one sequence unit and output at least one predicted next state for the sequence unit set. An interface generation engine generates an interface including at least one element related to the at least one predicted next state and transmits the interface to a user device associated with the user identifier.

    Systems and methods for webpage personalization

    公开(公告)号:US11811881B2

    公开(公告)日:2023-11-07

    申请号:US17578323

    申请日:2022-01-18

    摘要: A system can include one or more processing modules and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processing modules and perform receiving, from an electronic device, a search query from a user of a plurality of users; processing first data; and facilitating displaying a set of items. Processing the first data can comprise determining one or more keywords by capturing the one or more keywords during a time window; creating a feature set of second data associated with at least a portion of the plurality of users; determining a set of items of the item set as being based at least in part on an item vector representation and a keyword vector representation; determining a respective purchase probability associated with each item of the set of items of the item set; ranking the set of items.