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Dynamic credibility calculation to improve e-commerce reliability

IP.com Disclosure Number: IPCOM000198779D
Publication Date: 2010-Aug-16
Document File: 4 page(s) / 60K

Publishing Venue

The IP.com Prior Art Database

Abstract

This article describes a system and method to , in a buyers' feedback based credibility management system in e-commence, for buyer to get store's dynamic creditability according to buyer's input and the input time. Creditability system in my C2C sites is based on feedback history. For each deal, buyer will provide positive/neutral/negative feedback. When people want to buy something, they will evaluate the seller's credibility based on the buyers' historical feedback. For many stores in the c2c website, a seller sells many kind of goods, Buyers do not only care about the overall credit of the store, but also care more about the credit which is tightly related to the merchandise they wants to buy. In our system, the credibility is calculated based on the key words and other choices (such as category) used by a buyer when searching merchandise, and the calculation algorithm is weighted by the matching percentage of the merchandise and the different weights assigned for different feedback periods. The calculation weight can be predefined by the website for all buyers, and it also can be configured by each buyers.

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Dynamic credibility calculation to improve e

Dynamic credibility calculation to improve eDynamic credibility calculation to improve e -

When consumers want to buy something on a c 2c e-commerce website such as eBay and TaoBao , it's very important for them to know the credibility of a seller before placing orders . Credibility system, either eBay or TaoBao , is based on feedback history . For each deal, buyer will provide positive /neutral/negative feedback. When people want to buy something, they will evaluate the seller 's credibility based on the buyers ' historical feedback.

However, for many stores in the c 2c website, a seller sells many kind of goods . For example, a seller could sell cell phones and also sell digital cameras . The seller has different suppliers for different goods , and the qualities could be different . It's possible he has sold a lot of cellphones and got high credits , but only a small number of digital cameras without many credits from it . Therefore, if the consumer wants to buy a digital camera from this seller , it will be risky even the seller 's overall credit is very good. Even for the cell phones category , if I want to buy iphone , good quality might be not guaranteed , because this seller could have sold a lot of Nokia cellphones but few iphones.

So in the real world, the buyers do not only care about the overall credit of the store , but also care more about the credit which is tightly related to the merchandise they wants to buy. When they are searching the merchandise in the c 2c website, they prefer stores with their good credit related to these merchandise instead of the overall one , so they can easily pick up the store with the highest credit for the merchandise .

However in the current creditability system of c 2c websites, when you review the credits of the store, you will get an overall credits which the store got for all his historic data. If you want to know this store 's credit for the items you really care , you must retrieve all the store 's historic feedbacks and analyze the credits manually by yourselves. It is time consuming and is not user -friendly.

The root cause of above problems is that the credibility calculation is static and does not take individual's concern into consideration .

This disclosure presents a way , in a buyers' feedback based credibility management system in e-commence, for buyer to get store 's dynamic creditability according to buyer 's input and the input time.

The basic idea is that the credibility is calculated based on the key words and other choices (such as category) used by a buyer when searching merchandise . The keywords input by the buyer exactly reflects the buyer 's concern. Also, some buyers might think that the credits for the not -100%-matched merchandise also contribute to the store 's creditability, and some buyers care more about the more recent feedback period (for example, feedbacks in the last 1 month or 3 months) instead the whole...