公开(公告)号：US11544767B2

公开(公告)日：2023-01-03

申请号：US17715214

申请日：2022-04-07

Applicant: SAS Institute Inc.

Inventor： Xuejun Liao ,  Patrick Nathan Koch

IPC: G06Q30/06 ,  G06F17/16 ,  G06Q30/02

Abstract: A computing device determines a recommendation. A confidence matrix is computed using a predefined weight value. (A) A first parameter matrix is updated using the confidence matrix, a predefined response matrix, a first step-size parameter value, and a first direction matrix. The predefined response matrix includes a predefined response value by each user to each item and at least one matrix value for which a user has not provided a response to an item. (B) A second parameter matrix is updated using the confidence matrix, the predefined response matrix, a second step-size parameter value, and a second direction matrix. (C) An objective function value is updated based on the first and second parameter matrices. (D) The first and second parameter matrices are trained by repeating (A) through (C). The first and second parameter matrices output for use in predicting a recommended item for a requesting user.

公开(公告)号：US11842379B2

公开(公告)日：2023-12-12

申请号：US18169592

申请日：2023-02-15

Applicant: SAS Institute Inc.

Inventor： Jonathan Lee Walker ,  Hardi Desai ,  Xuejun Liao ,  Varunraj Valsaraj

IPC: G06Q30/00 ,  G06Q30/0601 ,  G06N5/04

CPC classification number: G06Q30/0631 ,  G06N5/04

Abstract: The computing device obtains a training data set related to a plurality of historic user inputs associated with preferences of one or more services or items from an entity. For each of the one or more services or items, the computing device executes operations to train a plurality of models using the training data set to generate a plurality of recommended models, apply a validation data set to generate a plurality of predictions from the plurality of recommended models, obtain a weight of each metric of a plurality of metrics from the entity, obtain user inputs associated with user preferences, and determine a relevancy score for each metric. The computing device selects a recommended model based on the relevancy score of the selected metric or a combination of selected metrics, generates one or more recommendations for the users, and outputs the one or more generated recommendations to the users.

公开(公告)号：US20230267527A1

公开(公告)日：2023-08-24

申请号：US18169592

申请日：2023-02-15

Applicant: SAS Institute Inc.

Inventor： Jonathan Lee Walker ,  Hardi Desai ,  Xuejun Liao ,  Varunraj Valsaraj

IPC: G06Q30/0601 ,  G06N5/04

CPC classification number: G06Q30/0631 ,  G06N5/04

Abstract: The computing device obtains a training data set related to a plurality of historic user inputs associated with preferences of one or more services or items from an entity. For each of the one or more services or items, the computing device executes operations to train a plurality of models using the training data set to generate a plurality of recommended models, apply a validation data set to generate a plurality of predictions from the plurality of recommended models, obtain a weight of each metric of a plurality of metrics from the entity, obtain user inputs associated with user preferences, and determine a relevancy score for each metric. The computing device selects a recommended model based on the relevancy score of the selected metric or a combination of selected metrics, generates one or more recommendations for the users, and outputs the one or more generated recommendations to the users.

公开(公告)号：US11379743B2

公开(公告)日：2022-07-05

申请号：US17386853

申请日：2021-07-28

Applicant: SAS Institute Inc.

Inventor： Xuejun Liao ,  Patrick Nathan Koch ,  Shunping Huang ,  Yan Xu

IPC: G06N5/04 ,  G06Q30/06 ,  G06F17/16

Abstract: A computing device determines a recommendation. (A) A first parameter matrix is updated using a first direction matrix and a first step-size parameter value that is greater than one. The first parameter matrix includes a row dimension equal to a number of users of a plurality of users included in a ratings matrix and the ratings matrix includes a missing matrix value. (B) A second parameter matrix is updated using a second direction matrix and a second step-size parameter value that is greater than one. The second parameter matrix includes a column dimension equal to a number of items of a plurality of items included in the ratings matrix. (C) An objective function value is updated based on the first parameter matrix and the second parameter matrix. (D) (A) through (C) are repeated until the first parameter matrix and the second parameter matrix satisfy a convergence test.

公开(公告)号：US20250068927A1

公开(公告)日：2025-02-27

申请号：US18583837

申请日：2024-02-21

Applicant: SAS INSTITUTE INC.

Inventor： Xuejun Liao ,  Patrick Nathan Koch

IPC: G06N20/00

Abstract: A system, method, and computer-program product includes receiving an input comprising a plurality of pre-defined factor matrices and an implicit feedback dataset partitioned into a plurality of implicit feedback data subsets; distributing the input across a controller node and a plurality of worker nodes implemented in a distributed computing environment; and training a model using the controller node and the plurality of worker nodes, wherein training the model includes: initializing, by the controller node, a controller-specific user parameters matrix and a controller-specific item parameters matrix, broadcasting, by the controller node, the controller-specific user parameters matrix and the controller-specific item parameters matrix to each worker node of the plurality of worker nodes, and concurrently executing an aggregation model training algorithm at the controller node and a plurality of localized model training algorithms across the plurality of worker nodes until a training termination condition is satisfied.

公开(公告)号：US20220237685A1

公开(公告)日：2022-07-28

申请号：US17715214

申请日：2022-04-07

Applicant: SAS Institute Inc.

Inventor： Xuejun Liao ,  Patrick Nathan Koch

IPC: G06Q30/06 ,  G06Q30/02 ,  G06F17/16

Abstract: A computing device determines a recommendation. A confidence matrix is computed using a predefined weight value. (A) A first parameter matrix is updated using the confidence matrix, a predefined response matrix, a first step-size parameter value, and a first direction matrix. The predefined response matrix includes a predefined response value by each user to each item and at least one matrix value for which a user has not provided a response to an item. (B) A second parameter matrix is updated using the confidence matrix, the predefined response matrix, a second step-size parameter value, and a second direction matrix. (C) An objective function value is updated based on the first and second parameter matrices. (D) The first and second parameter matrices are trained by repeating (A) through (C). The first and second parameter matrices output for use in predicting a recommended item for a requesting user.

公开(公告)号：US20220138605A1

公开(公告)日：2022-05-05

申请号：US17386853

申请日：2021-07-28

Applicant: SAS Institute Inc.

Inventor： Xuejun Liao ,  Patrick Nathan Koch ,  Shunping Huang ,  Yan Xu

IPC: G06N5/04 ,  G06F17/16 ,  G06Q30/06

Abstract: A computing device determines a recommendation. (A) A first parameter matrix is updated using a first direction matrix and a first step-size parameter value that is greater than one. The first parameter matrix includes a row dimension equal to a number of users of a plurality of users included in a ratings matrix and the ratings matrix includes a missing matrix value. (B) A second parameter matrix is updated using a second direction matrix and a second step-size parameter value that is greater than one. The second parameter matrix includes a column dimension equal to a number of items of a plurality of items included in the ratings matrix. (C) An objective function value is updated based on the first parameter matrix and the second parameter matrix. (D) (A) through (C) are repeated until the first parameter matrix and the second parameter matrix satisfy a convergence test.