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.

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：US20210374132A1

公开(公告)日：2021-12-02

申请号：US17093885

申请日：2020-11-10

Applicant: salesforce.com, inc.

Inventor： Wenzhuo Yang ,  Jia Li ,  Chenxi Li ,  Latrice Barnett ,  Markus Anderle ,  Simo Arajarvi ,  Harshavardhan Utharavalli ,  Caiming Xiong ,  Richard Socher ,  Chu Hong Hoi

IPC: G06F16/2457 ,  G06N20/20

Abstract: Embodiments are directed to a machine learning recommendation system. The system receives a user query for generating a recommendation for one or more items with an explanation associated with recommending the one or more items. The system obtains first features of at least one user and second features of a set of items. The system provides the first features and the second features to a first machine learning network for determining a predicted score for an item. The system provides a portion of the first features and a portion of the second features to second machine learning networks for determining explainability scores for an item and generating corresponding explanation narratives. The system provides the recommendation for one or more items and corresponding explanation narratives based on ranking predicted scores and explainability scores for the items.