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    SYSTEMS AND METHODS FOR SEQUENTIAL RECOMMENDATION
    1.
    发明申请
    SYSTEMS AND METHODS FOR SEQUENTIAL RECOMMENDATION 有权

    公开(公告)号：US20230073754A1

    公开(公告)日：2023-03-09

    申请号：US17586451

    申请日：2022-01-27

    Applicant: salesforce.com, inc.

    Inventor： Yongjun Chen ,  Zhiwei Liu ,  Jia Li ,  Caiming Xiong

    IPC: G06K9/62

    Abstract: Embodiments described herein provides an intent prototypical contrastive learning framework that leverages intent similarities between users with different behavior sequences. Specifically, user behavior sequences are encoded into a plurality of user interest representations. The user interest representations are clustered into a plurality of clusters based on mutual distances among the user interest representations in a representation space. Intention prototypes are determined based on centroids of the clusters. A set of augmented views for user behavior sequences are created and encoded into a set of view representations. A contrastive loss is determined based on the set of augmented views and the plurality of intention prototypes. Model parameters are updated based at least in part on the contrastive loss.

    SELF-SUPERVISED LEARNING WITH MODEL AUGMENTATION
    2.
    发明申请
    SELF-SUPERVISED LEARNING WITH MODEL AUGMENTATION 有权

    公开(公告)号：US20230042327A1

    公开(公告)日：2023-02-09

    申请号：US17579377

    申请日：2022-01-19

    Applicant: salesforce.com, inc.

    Inventor： Zhiwei Liu ,  Caiming Xiong ,  Jia Li ,  Yongjun Chen

    IPC: G06N3/08 ,  G06N3/04

    Abstract: A method for providing a neural network system includes performing contrastive learning to the neural network system to generate a trained neural network system. The performing the contrastive learning includes performing first model augmentation to a first encoder of the neural network system to generate a first embedding of a sample, performing second model augmentation to the first encoder to generate a second embedding of the sample, and optimizing the first encoder using a contrastive loss based on the first embedding and the second embedding. The trained neural network system is provided to perform a task.

    SYSTEMS AND METHODS FOR NEXT BASKET RECOMMENDATION WITH DYNAMIC ATTRIBUTES MODELING
    4.
    发明申请
    SYSTEMS AND METHODS FOR NEXT BASKET RECOMMENDATION WITH DYNAMIC ATTRIBUTES MODELING 有权

    公开(公告)号：US20220058714A1

    公开(公告)日：2022-02-24

    申请号：US17112765

    申请日：2020-12-04

    Applicant: salesforce.com, inc.

    Inventor： Yongjun Chen ,  Jia Li ,  Chenxi Li ,  Markus Anderle ,  Caiming Xiong ,  Simo Arajarvi ,  Harshavardhan Utharavalli

    IPC: G06Q30/06 ,  G06N3/04 ,  G06N3/08

    Abstract: Embodiments described herein provide an attentive network framework that models dynamic attributes with item and feature interactions. Specifically, the attentive network framework first encodes basket item sequences and dynamic attribute sequences with time-aware padding and time/month encoding to capture the seasonal patterns (e.g. in app recommendation, outdoor activities apps are more suitable for summer time while indoor activity apps are better for winter). Then the attentive network framework applies time-level attention modules on basket items' sequences and dynamic user attributes' sequences to capture basket items to basket items and attributes to attributes temporal sequential patterns. After that, an intra-basket attentive module is used on items in each basket to capture the correlation information among items.

    Systems and methods for next basket recommendation with dynamic attributes modeling
    6.
    发明授权
    Systems and methods for next basket recommendation with dynamic attributes modeling 有权

    公开(公告)号：US11605118B2

    公开(公告)日：2023-03-14

    申请号：US17112765

    申请日：2020-12-04

    Applicant: salesforce.com, inc.

    Inventor： Yongjun Chen ,  Jia Li ,  Chenxi Li ,  Markus Anderle ,  Caiming Xiong ,  Simo Arajarvi ,  Harshavardhan Utharavalli

    IPC: G06Q30/00 ,  G06Q30/0601 ,  G06N3/08 ,  G06N3/04

    Abstract: Embodiments described herein provide an attentive network framework that models dynamic attributes with item and feature interactions. Specifically, the attentive network framework first encodes basket item sequences and dynamic attribute sequences with time-aware padding and time/month encoding to capture the seasonal patterns (e.g. in app recommendation, outdoor activities apps are more suitable for summer time while indoor activity apps are better for winter). Then the attentive network framework applies time-level attention modules on basket items' sequences and dynamic user attributes' sequences to capture basket items to basket items and attributes to attributes temporal sequential patterns. After that, an intra-basket attentive module is used on items in each basket to capture the correlation information among items.

    Data privacy protected machine learning systems
    8.
    发明授权
    Data privacy protected machine learning systems 有权

    公开(公告)号：US11568306B2

    公开(公告)日：2023-01-31

    申请号：US16398757

    申请日：2019-04-30

    Applicant: salesforce.com, inc.

    Inventor： Lichao Sun ,  Caiming Xiong ,  Jia Li ,  Richard Socher

    IPC: G06K9/62 ,  G06N20/00 ,  G06F21/62 ,  G06N3/08

    Abstract: Approaches for private and interpretable machine learning systems include a system for processing a query. The system includes one or more teacher modules for receiving a query and generating a respective output, one or more privacy sanitization modules for privacy sanitizing the respective output of each of the one or more teacher modules, and a student module for receiving a query and the privacy sanitized respective output of each of the one or more teacher modules and generating a result. Each of the one or more teacher modules is trained using a respective private data set. The student module is trained using a public data set. In some embodiments, human understandable interpretations of an output from the student module is provided to a model user.

    Robustness evaluation via natural typos
    9.
    发明授权
    Robustness evaluation via natural typos 有权

    公开(公告)号：US11669712B2

    公开(公告)日：2023-06-06

    申请号：US16559196

    申请日：2019-09-03

    Applicant: salesforce.com, inc.

    Inventor： Lichao Sun ,  Kazuma Hashimoto ,  Jia Li ,  Richard Socher ,  Caiming Xiong

    IPC: G06N3/08 ,  G06F40/232 ,  G06N3/045 ,  G06N3/008 ,  G06N3/044

    CPC classification number: G06N3/008 ,  G06F40/232 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08

    Abstract: A method for evaluating robustness of one or more target neural network models using natural typos. The method includes receiving one or more natural typo generation rules associated with a first task associated with a first input document type, receiving a first target neural network model, and receiving a first document and corresponding its ground truth labels. The method further includes generating one or more natural typos for the first document based on the one or more natural typo generation rules, and providing, to the first target neural network model, a test document generated based on the first document and the one or more natural typos as an input document to generate a first output. A robustness evaluation result of the first target neural network model is generated based on a comparison between the output and the ground truth labels.

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