case-based reasoning in e-commerce joe souto cse 435
TRANSCRIPT
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Case-Based Reasoning in E-Commerce
Joe SoutoCSE 435
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What is E-Commerce?
“The exchange of information, goods, or services through electronic networks”1
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How can CBR help?
How many times have you seen this?
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How can CBR help?
Or this?
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What’s wrong?
Demand is either over-specified or under-specified
It is up to the user to find what they want
There is no intelligent sales support
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We have a problem
Buyer has limited knowledge of product base
Seller has limited knowledge of buyer’s requirements”Knowledge Gap”
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We have a problem
Knowledge gap is solved in real-life by a human sales agent as a mediator. We don’t have this luxury online.
Solution: CBR approach product knowledge is stored as experience in a case base.
Sales agent makes recommendations based on the stored experience.
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Some Preliminary Info
We need a way to define user requirements
Customers buy items in order to satisfy their desires
Define a customer’s desire as a “Wish” Wishes have various properties
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Individual Wish Properties
Importance Hard: MUST be met (ie: “vacation for <$2000”) Soft: not essential, but helpful (ie: “red” car)
Agent must satisfy ALL hard req’s and as many soft as possible
Precision Precisely Determined (specific, ie: “>3GHz P4”) Undetermined (vague, ie: “fast processor”)
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Individual Wish Properties
Certainty Certain Uncertain
Sales agent must try to increase certainty of wishes and make recommendations based on them
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Overall Wish Properties
Redundancy Wishes can be redundant
Ex: Computer that’s “fast” and can play Half-Life 2 Agent must recognize and avoid redundant
inquiries
Consistency Wishes can be contradictory
Ex: new Ferrari, and under $1000 Agent must either ask user to clarify, or suggest
products that satisfy one of the two wishes
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Product Classifications
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How Do These Properties Help?
1. Customers want a product to satisfy a wish
2. Products have various properties
3. Therefore, product properties can be mapped to the satisfaction of a customer’s wishWith all that in mind, now we
can look at the transaction process
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Transaction Model
Single transaction can be modeled with three phases
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Pre-Sales
Buyer wants a product, Seller provides information
3 Phases Supplier Search
Client determines which supplier can satisfy their wishes
Product Search Mapping of customer criteria to products
Negotiation1. Price and way of payment 2. Details of delivery 3. Regulations about cost and delivery
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Pre-Sales
Recall the Google Example
No “intelligent sales support”
Burden of knowledge is in hands of the customers
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Example
Due to Knowledge Gap, Analog Devices added a CBR system to assist Pre-Sales
Analog Devices:http://www.analog.com
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How Does It Work?
Similarity Metrics! Similarity function
for single attribute OK to be under, less
similar if over desired value
The overall similarity is computed weighted average of local similarities.
Remember the “priority” box
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Sales
Product has been chosen, must be configured and paid for
Customer and Sales Agent negotiate about product attributes and costs
Intelligent Support is needed for negotiation
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Negotiation “A process where two parties bargain
resources for an intended gain”1
In Sales phase, customers navigate through products to satisfy their wish.
Some wishes known, others discovered in the process. Hard wishes must be fulfilled, soft wishes can be negotiated. Agent finds out these demands with the customer and finds a product which fulfills them. Agent can be “Active” or “Passive”
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Sales
CBR Model must be modified Standard Model:
2. Reuse3. Revise
4. Retain
Case Library
1. RetrieveBackground Knowledge
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Sales
New Model No Retain phase: sale
does not add another product to the product base
Add Refine phase: user demands refined based on the evaluations given by the customer.
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Example
CBR approach to negotiating a BMW sale
Agent here is passive
Buttons for “sportier”, “more comfortable”, “cheaper”, etc.
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After-Sales
Customer has already bought a product and needs support during its usage
To assist the customer, they are supported with a case base of possible product problems, a query interface, and similarity measures which should help to find a similar problem and solution
Many companies have online CBR customer-support websites (Dell, 3Com, etc) Help Desk Systems
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Example
Dell Support site:http://support.dell.com
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Summary
E-commerce is a growing field with lots of potential revenue
Standard search technology is too limited
CBR can be applied in all 3 transaction phases
Key is to provide intelligent sales support agent guides customer through each phase of transaction
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References
1. “Intelligent Sales Support with CBR”Wilke, Lenz, Wess
2. “Experience Management for Electronic Commerce”Bergmann
3. Wikipedia: http://en.wikipedia.org/wiki/E-commerce