公开(公告)号：US20210073833A1

公开(公告)日：2021-03-11

申请号：US17015822

申请日：2020-09-09

Applicant: eBay Inc.

Inventor： Maxim Manco ,  Fang Fang ,  Natraj Srinivasan ,  Alexander Akerman ,  Michael Ebin ,  Eran Ben Tovim

IPC: G06Q30/02 ,  G06Q50/04 ,  G06Q30/06 ,  G06Q10/08 ,  G06N3/04 ,  G06N3/08 ,  G05B13/02 ,  G06F3/12 ,  B33Y50/02

Abstract: An autonomous item generation system implements a trained machine learning model configured to output fabrication instructions for generating an item and metadata describing the item, automatically and independent of user input. Fabrication instructions output by the machine learning model are transmitted to a fabrication device for generating the item. The autonomous item generation system generates a listing for the item based on the metadata output by the machine learning model and publishes the listing to a virtual marketplace. Analytics data describing feedback for the item listing is used to generate training data for the machine learning model. The training data is input to the machine learning model, which causes the machine learning model to refine at least one control parameter according to a loss function that penalizes negative differences between predicted and observed feedback data for the item. The machine learning model with the refined parameter(s) is then used by the autonomous item generation system to generate fabrication instructions and metadata for an additional item.

公开(公告)号：US20230098794A1

公开(公告)日：2023-03-30

申请号：US18061740

申请日：2022-12-05

Applicant: eBay Inc.

Inventor： Maxim Manco ,  Fang Fang ,  Natraj Srinivasan ,  Alexander Akerman ,  Michael Ebin ,  Eran Ben Tovim

IPC: G06Q30/06 ,  G06F9/451 ,  B33Y50/02 ,  G05B13/02 ,  G06Q30/02 ,  G06F3/12 ,  G06N3/08 ,  G06F21/62 ,  G06Q50/04 ,  G06N20/00 ,  G06Q10/08 ,  G06N3/04 ,  G06F3/0482 ,  G06F3/0484

Abstract: An autonomous item generation system implements a trained machine learning model configured to output fabrication instructions for generating an item and metadata describing the item, automatically and independent of user input. Fabrication instructions output by the machine learning model are transmitted to a fabrication device for generating the item. The autonomous item generation system generates a listing for the item based on the metadata output by the machine learning model and publishes the listing to a virtual marketplace. Analytics data describing feedback for the item listing is used to generate training data for the machine learning model. The training data is input to the machine learning model, which causes the machine learning model to refine at least one control parameter according to a loss function that penalizes negative differences between predicted and observed feedback data for the item. The machine learning model with the refined parameter(s) is then used by the autonomous item generation system to generate fabrication instructions and metadata for an additional item.

公开(公告)号：US20170293695A1

公开(公告)日：2017-10-12

申请号：US15190279

申请日：2016-06-23

Applicant: eBay Inc.

Inventor： Yuri Michael Brovman ,  Marie Jacob ,  Natraj Srinivasan ,  Stephen Neola ,  Daniel Galron ,  Ryan Snyder ,  Paul Wang

IPC: G06F17/30

CPC classification number: G06Q30/0631 ,  G06Q30/0251

Abstract: Systems, methods and media are provided for optimizing similar item recommendations in a semi-structured environment. In one embodiment a system includes at least one processor and a memory storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising, at least identifying a seed item; retrieving a subset of recommended items relevant to the seed item; and ranking the subset of recommended items based on an item conversion probability, wherein the ranking of the subset of recommended items is based on a machine learning technique, and wherein a binary or multi-class label is used as training input to the machine learning technique.