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.

公开(公告)号：US20230252345A1

公开(公告)日：2023-08-10

申请号：US17827334

申请日：2022-05-27

Applicant: salesforce.com, inc.

Inventor： Yongjun Chen ,  Jia LI ,  Nitish Shirish Keskar ,  Caiming Xiong

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Embodiments described herein provide methods and systems for training a sequential recommendation model. A system receives a plurality of user behavior sequences, and encodes those sequences into a plurality of user interest representations. The system predicts a next item using a sequential recommendation model, producing a probability distribution over a set of items. The next interacted item in a sequence is selected as a positive sample, and a negative sample is selected based on the generated probability distribution. The positive and negative samples are used to compute a contrastive loss and update the sequential recommendation model.

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.

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.

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.