公开(公告)号：US20200020013A1

公开(公告)日：2020-01-16

申请号：US16450537

申请日：2019-06-24

Applicant: eBay Inc.

Inventor： Elizabeth Churchill ,  Hugo Liu

IPC: G06Q30/06

Abstract: A system to customize user experience based on brand resilience data is described. An example system includes a new session detector, a session type module, a brand resilience module, and a search strategy selector. The new session detector detects commencement of a user session in the on-line trading platform. The session type module examines the initial search request in a user session and determines whether the initial search request includes a phrase that represents a brand name. The brand resilience module examines brand resilience value assigned to the brand name. The search strategy selector selects, based on the brand resilience value, a search strategy for the user session.

公开(公告)号：US20160092893A1

公开(公告)日：2016-03-31

申请号：US14563828

申请日：2014-12-08

Applicant: eBay Inc.

Inventor： Hugo Liu ,  Elizabeth Churchill

IPC: G06Q30/02 ,  G06F17/30

CPC classification number: G06Q30/0202 ,  G06F16/9535 ,  G06Q30/0631

Abstract: The present disclosure is directed to apparatuses, systems, and methods for predicting item characteristic popularity—i.e., identifying item characteristics (e.g., item brands, item types, etc.) that are to eventually become popular. Something that is to eventually become popular is referred to herein as “pre-trend” or “cool.” In the embodiments described herein, electronic marketplace transaction data is analyzed to identify popular characteristics of items involved in recent transactions. The electronic marketplace transaction data is further analyzed to identify one or more users that executed transactions for items having these popular characteristics during a previous time period. These users' transaction histories are analyzed to determine what other item characteristics are prevalent in their more recent transactions, as these item characteristics can be identified as pre-trend/cool.

Abstract translation: 本公开涉及用于预测项目特征流行度的装置，系统和方法，即识别最终变得流行的项目特征（例如，项目品牌，项目类型等）。 最终变得流行的东西在本文中被称为“前趋势”或“酷”。在本文所述的实施例中，分析电子市场交易数据以识别最近交易中涉及的物品的流行特征。 进一步分析电子市场交易数据以识别在前一时间段内对具有这些流行特征的物品执行交易的一个或多个用户。 对这些用户的交易历史进行分析，以确定其最近交易中其他项目特征是否普遍存在，因为这些项目特征可以被识别为前趋势/趋势。