MACHINE LEARNING-BASED ITEM FEATURE RANKING
    1.
    发明申请

    公开(公告)号:WO2021258061A1

    公开(公告)日:2021-12-23

    申请号:PCT/US2021/038282

    申请日:2021-06-21

    Abstract: A method of determining item features and organizing an interface according to the features includes determining a plurality of features of a plurality of items, the plurality of items accessible through an electronic interface, applying a plurality of machine learning models to the determined features, wherein each of the machine learning models calculates a correlation of each feature to characteristic determinative of user selection on the electronic interface, calculating respective Shapley values of each correlation determined by each of the plurality of machine learning models, determining one or more of the item features that are most strongly correlated with the determinative characteristic according to the respective Shapley values, and causing the electronic interface to be organized according to the determined most strongly correlated item features.

    METHODS FOR IDENTIFYING PRODUCT VARIANTS
    3.
    发明申请

    公开(公告)号:WO2021216526A1

    公开(公告)日:2021-10-28

    申请号:PCT/US2021/028121

    申请日:2021-04-20

    Abstract: A computer-implemented method includes extracting, by one or more processors of one or more computing devices, a product family name from each of a plurality of unstructured product titles associated with a plurality of products. The method further includes determining, by the one or more processors, a degree of similarity between model numbers of the plurality of products. The method further includes determining, by the one or more processors, that at least two of the plurality of products are variants of one another by determining that the at least two of the plurality of products have a same extracted product family name and determining that the degree of similarity between the model numbers of the plurality of products is above a predetermined threshold.

    USER CLICK MODELLING IN SEARCH QUERIES
    4.
    发明申请

    公开(公告)号:WO2022094377A1

    公开(公告)日:2022-05-05

    申请号:PCT/US2021/057518

    申请日:2021-11-01

    Abstract: A method for ranking documents in search results includes defining a first training data set, the first training data set including, for each of a plurality of user queries, information respective of a document selected by a user from results responsive to the query and information respective of one or more documents within an observation window after the selected document in the results, and defining a second training data set, the second training data set including, for each of the plurality of user queries, information respective of the selected document. The method further includes training a first machine learning model with the first training data set, training a second machine learning model with the second training data set, and ranking documents of a further search result set according to the output of the first machine learning model and the output of the second machine learning model.

    COMPLEMENTARY ITEM RECOMMENDATIONS BASED ON MULTI-MODAL EMBEDDINGS

    公开(公告)号:WO2021046372A1

    公开(公告)日:2021-03-11

    申请号:PCT/US2020/049439

    申请日:2020-09-04

    Abstract: Systems and methods for providing suggestions of complementary products responsive to an anchor product are disclosed. The method includes receiving a selection of an anchor product. A similarity score between text embeddings of the anchor product and text embeddings of a plurality of products in a product database is calculated. A similarity score between an image feature of the anchor product and an image feature of the plurality of products in the product database is calculated. A weighted score between the two similarity scores as calculated for the anchor product and the plurality of products in the product database is calculated. At least one of the products from the product database having a highest weighted score is selected and returned responsive to the selection of the anchor product.

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