公开(公告)号：US20230079147A1

公开(公告)日：2023-03-16

申请号：US18055574

申请日：2022-11-15

Applicant: eBay Inc.

Inventor： Sanjika Hewavitharana ,  Evgeny Matusov ,  Robinson Piramuthu ,  Hassan Sawaf

IPC: G06Q30/06 ,  G06F40/58 ,  G06N20/00 ,  G06F16/50

Abstract: In various example embodiments, a system and method for a Listing Engine that translates a first listing from a first language to a second language. The first listing includes an image(s) of a first item. The Listing Engine provides as input to an encoded neural network model a portion(s) of a translated first listing and a portions(s) of a second listing in the second language. The second listing includes an image(s) of a second item. The Listing Engine receives from the encoded neural network model a first feature vector for the translated first listing and a second feature vector for the second listing. The first and the second feature vectors both include at least one type of image signature feature and at least one type of listing text-based feature. Based on a similarity score of the first and second feature vectors at least meeting a similarity score threshold, the Listing Engine generates a pairing of the first listing in the first language with the second listing in the second language for inclusion in training data of a machine translation system.

公开(公告)号：US10319019B2

公开(公告)日：2019-06-11

申请号：US15264873

申请日：2016-09-14

Applicant: eBay Inc.

Inventor： Sanjika Hewavitharana ,  Evgeny Matusov ,  Robinson Piramuthu ,  Hassan Sawaf

IPC: G06Q30/00 ,  G06Q30/06 ,  G06F17/28 ,  G06F16/50

Abstract: In various example embodiments, a system and method for a Listing Engine that translates a first listing from a first language to a second language. The first listing includes an image(s) of a first item. The Listing Engine provides as input to an encoded neural network model a portion(s) of a translated first listing and a portions(s) of a second listing in the second language. The second listing includes an image(s) of a second item. The Listing Engine receives from the encoded neural network model a first feature vector for the translated first listing and a second feature vector for the second listing. The first and the second feature vectors both include at least one type of image signature feature and at least one type of listing text-based feature. Based on a similarity score of the first and second feature vectors at least meeting a similarity score threshold, the Listing Engine generates a pairing of the first listing in the first language with the second listing in the second language for inclusion in training data of a machine translation system.

公开(公告)号：US20180068031A1

公开(公告)日：2018-03-08

申请号：US15677817

申请日：2017-08-15

Applicant: eBay Inc.

Inventor： Sanjika Hewavitharana ,  Ajinkya Gorakhnath Kale

IPC: G06F17/30 ,  G06F17/21 ,  G06F15/18 ,  G06F11/34 ,  G06F9/30

CPC classification number: G06F16/90328 ,  G06F9/30156 ,  G06F11/3438 ,  G06F16/3329 ,  G06F16/9535 ,  G06F17/21 ,  G06F17/278 ,  G06F17/2785 ,  G06F17/289 ,  G06N3/006 ,  G06N3/08 ,  G06N5/022 ,  G06N7/005 ,  G06N20/00 ,  G10L15/1822 ,  G10L15/22

Abstract: In various example embodiments, a system and method for balancing implicit and explicit indicators is presented. In one example, a method includes a user's utterance that includes a user query, extracting user intent parameters from the user query, constructing a structured query in a machine readable format using user intent parameters, determining whether the user query includes an implicit indicator, determining a confidence rating for the implicit indicator, updating the structured query to include the implicit indicator in response to the confidence rating for the implicit indicator being above a threshold value, and submitting the structured query to a database of items.

公开(公告)号：US20180052884A1

公开(公告)日：2018-02-22

申请号：US15238679

申请日：2016-08-16

Applicant: eBay Inc.

Inventor： Ajinkya Gorakhnath Kale ,  Sanjika Hewavitharana

IPC: G06F17/30 ,  G06N5/04

CPC classification number: G06F16/242 ,  G06F16/24578 ,  G06F16/9024 ,  G06F17/27 ,  G06N5/022 ,  G06N5/04 ,  G06Q30/0631

Abstract: Processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation. Analyses of user inputs are coordinated via a knowledge graph constructed from categories, attributes, and attribute values describing relatively frequently occurring prior interactions of various users with an electronic marketplace. The knowledge graph has directed edges each with a score value based on: the conditional probabilities of category/attribute/attribute value interactions calculated from user behavioral patterns, associations between user queries and structured data based on historical buyer behavioral patterns in the marketplace, metadata from items made available for purchase by sellers used to better define buyers' requirements, and/or world knowledge of weather, locations/places, occasions, and item recipients that map to inventory-related data, for generating relevant prompts for further user input. The knowledge graph may be dynamically updated during a multi-turn interactive dialog.

公开(公告)号：US20180052842A1

公开(公告)日：2018-02-22

申请号：US15238675

申请日：2016-08-16

Applicant: eBay Inc.

Inventor： Sanjika Hewavitharana ,  Ajinkya Gorakhnath Kale ,  Saab Mansour

IPC: G06F17/30 ,  G06F17/27 ,  G06F17/28 ,  G10L15/26

CPC classification number: G06F16/24522 ,  G06F16/243 ,  G06F16/951 ,  G06F17/2705 ,  G06F17/273 ,  G06F17/278 ,  G06F17/2785 ,  G06F17/2836 ,  G06F17/289 ,  G10L15/26

Abstract: Systems and methods for transforming formal and informal natural language user inputs into a more formal, machine-readable, structured representation of a search query. In one scenario, a processed sequence of user inputs and machine-generated prompts for further data from a user in a multi-turn interactive dialog improves the efficiency and accuracy of automated searches for the most relevant items available for purchase in an electronic marketplace. Analysis of user inputs may discern user intent, user input type, a dominant object of user interest, item categories, item attributes, attribute values, and item recipients. Other inputs considered may include dialog context, item inventory-related information, and external knowledge to improve inference of user intent from user input. Different types of analyses of the inputs each yield results that are interpreted in aggregate and coordinated via a knowledge graph based on past users' interactions with the electronic marketplace and/or inventory-related data.