Agents for e-Commerce
-
Upload
kusumsharma8 -
Category
Documents
-
view
224 -
download
0
Transcript of Agents for e-Commerce
![Page 1: Agents for e-Commerce](https://reader034.fdocuments.us/reader034/viewer/2022051009/577d2b9d1a28ab4e1eaae732/html5/thumbnails/1.jpg)
8/7/2019 Agents for e-Commerce
http://slidepdf.com/reader/full/agents-for-e-commerce 1/6
Agents for e-CommerceJane Hsu
Motivation for Agents in E-Commerce
p Task-delegation
p Personalized
p Adaptive
p Continuously running
p Semi-autonomous
3
CBB Model (Pattie Maes, et al ) Classification of Agents: Market View
processes of sales ⇒ categories for agents:
p Demand Identificationp Awareness of the need to buy
p Product Brokeringp What to buy
p Merchant Brokeringp Who to buy from
p Negotiationp How much to pay
p Purchase and Deliveryp Payment and delivery options
p Product Service and Evaluationp Service reminder and tracking
E-Commerce Agents
p Personal shopping assistants
n Price comparison
n Compatibility
n Purchase/warrantee information
p Distributed negotiation agents
p Auction Bots
p Stock Bots
p Recommendation and notification
p Agent-mediated electronic commerce
Agent-Mediated E-Commerce [MIT]
p C2C smart classified ads
p Merchant agents
n Integrative negotiation capabilities
p Expertise brokering
p Distributed reputation facilities
![Page 2: Agents for e-Commerce](https://reader034.fdocuments.us/reader034/viewer/2022051009/577d2b9d1a28ab4e1eaae732/html5/thumbnails/2.jpg)
8/7/2019 Agents for e-Commerce
http://slidepdf.com/reader/full/agents-for-e-commerce 2/6
Agents as Mediators in E-Commerce
Y
Personal
Logic
Y
Auction Bot
Y
Firefly
Y
Bargain
Finder
6. Product
Service and
Evaluation
5. Purchase and
Delivery
YY4. Negotiation
YY3. Merchant
Brokering
YY2. ProductBrokering
1. Need
Identification
eBayKasbahExcite
Jango
Overview
p 1st generation agents
n Filter information
n Match people w/similar interestsn Automate repetitive behavior
p 2nd generation
n E-commerce ==> revolutionizep business-to-business
p business-to-consumer
p consumer-to-consumer
Price-Comparison Shopping Agents
p BargainFinder was the first shopping agent foron-line price comparisons.
p Given a specific music CD, BargainFinderrequests its price from each of nine differentmerchant Web sites using the same request asfrom a Web browser. BargainFinder then presentsits results to the consumer.
p Like most of the first generation of e-commercesystems, BargainFinder do not exist anymore.However it offers valuable insights into the issuesinvolved in product comparisons in the onlineworld.
p Limited to comparing merchants offering only onprice instead of their full range of value
Excite's Jango
p Jango is similar to BargainFinder but with moreproduct features to search across and moreshopping categories.n Help user decide what to buy.n Finding specs and reviews of products.n Make recommendations.n Comparison shopping for best buy.n Monitoring “what’s new” lists.n Watching for special offers & discounts.
p Jango solves the merchant blocking issue byhaving the product requests originating fromeach consumer's Web browser instead of acentralised site as in BargainFinder appear asrequests from real customers
![Page 3: Agents for e-Commerce](https://reader034.fdocuments.us/reader034/viewer/2022051009/577d2b9d1a28ab4e1eaae732/html5/thumbnails/3.jpg)
8/7/2019 Agents for e-Commerce
http://slidepdf.com/reader/full/agents-for-e-commerce 3/6
Example: MySimon
p Comparison shopping agentn User enters or chooses
description of what they arelooking for.
n mySimon presents matchesfrom merchant databases.
p Agent learns to searchn Supposedly, agent learns how
to retrieve desired informationfrom merchant websites.
n Non-programmers interactwith system to teach it how toretrieve prices from new sites.
![Page 4: Agents for e-Commerce](https://reader034.fdocuments.us/reader034/viewer/2022051009/577d2b9d1a28ab4e1eaae732/html5/thumbnails/4.jpg)
8/7/2019 Agents for e-Commerce
http://slidepdf.com/reader/full/agents-for-e-commerce 4/6
Aristocart by Kanndu
p Conducting searches according to the criteria youspecify.
p Automatically running price comparisons on the
items you select, in order to find the best price.p Constantly monitoring the Web to see whether
prices have changed.p Enabling you to make all your purchases,
whether from one online store or many, using asingle, two-click checkout procedure.
p Download Aristocart
20
User
Information Agent
Internet
維科 SERVER
華南銀行 SERVER
Request =``java’’
Queryplanner
planexecutor
URL request
URL request
Extractor華南銀行
ExtractorWiley
Extractor維科
Thanks!
Query plan:define orders and steps toreply user’s request
Domain
model
Define the domain:
objects and theirrelations
Sourcemodel
Configuration file:describe connected
Web sites
Amazon?
Wiley SERVER
Extracted data
Extracted dataConfigureAMAZON?
ExtractorAMAZON?
Connectinga new Web site!
Recommender Systems
p Content-based filteringn Collects information from various sourcesn Synthesizes information
p Collaborative filteringn Use information about other customers to recommend
p Constraint-based filteringn Special case of content-based
n Optimization problem within constraints
Firefly
p Firefly services help consumers find products.
p Instead of filtering products based on features,Firefly recommends products via a word of mouthrecommendation mechanism called automatedcollaborative filtering (ACF).
p ACF first compares a shopper's product ratingswith those of other shoppers. After identifyingthe shopper's nearest neighbours (i.e., userswith similar tastes), ACF recommends productsthat they rated highly.
p Essentially, Firefly uses the opinions of like-minded people to offer recommendations.
p Firefly was acquired by Microsoft in 1998.
![Page 5: Agents for e-Commerce](https://reader034.fdocuments.us/reader034/viewer/2022051009/577d2b9d1a28ab4e1eaae732/html5/thumbnails/5.jpg)
8/7/2019 Agents for e-Commerce
http://slidepdf.com/reader/full/agents-for-e-commerce 5/6
Basic calculations in collaborative filtering
a – customer 1’s preference vector
b – customer 2’s preference vector
Collaborative Filtering Agents As Mediators
Role
Buyer Seller
*Mediator Wrapper
*consults queries
Recommender Agents
Buyer Recommender
Buyer
Buyer
User
User
User
User profilezip, age, genderinterests
constraints
Catalog ofitemsitem 1
item 2...
Software Design as Problem Solving
DomainProblem
EndUser
SoftwareDesigner
The Soloist Model
programmer Computer
The Conductor Model
programmer
Computer
Computer
Computer
![Page 6: Agents for e-Commerce](https://reader034.fdocuments.us/reader034/viewer/2022051009/577d2b9d1a28ab4e1eaae732/html5/thumbnails/6.jpg)
8/7/2019 Agents for e-Commerce
http://slidepdf.com/reader/full/agents-for-e-commerce 6/6
The Manager Model
EndUser
Agent
InterfaceAgent
Agent
Agent
Agent-Oriented Design
p A collection of task-specific agents
p Multi-agent architecture
p Agent communication
n KQML
n Eureka (KQML/ContractNet Protocols)
Agent Design Requirement
p Perceptions
p Actions
p Architecture
n Reactive
n Deliberative
n ??
p Functions performed by the agent
p A sample scenario of the agent in action
Agents: Shopping Domain
p Airfare data collection agent
p Price mining agent
p Ticket purchase agent
p Travel planning agent
p Recommender agent
p Product alert agent
To Buy or Not to Buy?
p Question: Should I buy the ticket now?
n To learn the behavior of airfares over time
p Collect airfare data
p Induce airline pricing model
Airline BPricing Model
Travel Web
Airline A
Pricing Model
Airfare Data
ShoppingAgent
DestinationConstraintsPreferences
Comparison Shopping
p ShotBot
p Price Mining
n Rule learning
n Q-learning
n Moving average models
n Combination of methods