公开(公告)号：US11216515B2

公开(公告)日：2022-01-04

申请号：US15836744

申请日：2017-12-08

Applicant: eBay Inc.

Inventor： Ishita Kamal Khan ,  Prathyusha Senthil Kumar ,  Daniel Miranda ,  David Goldberg

IPC: G06F16/9535 ,  G06N7/00 ,  G06F16/9038 ,  G06F16/2457 ,  G06N20/00 ,  G06F16/9536 ,  G06F16/33 ,  G06Q30/06

Abstract: Various methods and systems for providing query result items using an item title demand model are provided. A query is received at a search engine. Based on receiving the query, an item title demand engine is accessed. The item title demand engine operates based on an item title demand model which uses token weights, representing skip probabilities of tokens in item titles, to determine title scores for result item titles for corresponding queries. Based on accessing the item title demand engine, one or more result item titles for the query are identified from items in an item database. An identified result item title is identified based on a title score determined using the item title demand model and a highest skip probability of a token in the result item title. The one or more result item titles are communicated to cause display of the one or more result item titles.

公开(公告)号：US20190392082A1

公开(公告)日：2019-12-26

申请号：US16017700

申请日：2018-06-25

Applicant: eBay Inc.

Inventor： Anthony Bell ,  Daniel Miranda ,  Prathyusha Senthil Kumar

IPC: G06F17/30 ,  G06Q30/06 ,  G06N99/00

Abstract: A query for one or more resources is received. One or more tokens associated with the query is identified based on running the query through a learning model. The one or more tokens correspond to one or more terms that the query shares context similarity to based on a history of user selections. One or more search result candidates are scored based at least on the context similarity between the one or more tokens and the query.

公开(公告)号：US11204972B2

公开(公告)日：2021-12-21

申请号：US16017700

申请日：2018-06-25

Applicant: eBay Inc.

Inventor： Anthony Bell ,  Daniel Miranda ,  Prathyusha Senthil Kumar

IPC: G06Q30/00 ,  G06F16/9535 ,  G06Q30/06 ,  G06N20/00 ,  G06F16/2457

Abstract: A query for one or more resources is received. One or more tokens associated with the query is identified based on running the query through a learning model. The one or more tokens correspond to one or more terms that the query shares context similarity to based on a history of user selections. One or more search result candidates are scored based at least on the context similarity between the one or more tokens and the query.