c ontent-oriented negotiation in e-c ommerce
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CONTENT-ORIENTED NEGOTIATION IN E-COMMERCE
Boğaziçi University Department of Computer Engineering
Reyhan Aydoğan
Thesis Advisor: Asst. Prof. Pınar Yolum
2
OUTLINE
Negotiation Architecture Technical Details
Representation Learning Phase Similarity Estimation Offering Service Mechanism
Developed System & Performance Evaluation Discussion
3
Negotiation Architecture
Consumer Agent
<Preferences><price v=low/><speed v=high/>……………</Preferences>
?
Producer Agent
?
SHAREDONTOLOGY
Data Repository(Inventory
Information)
1- Request 2-Evaluate Request and Learning
4-Evaluate the offer
5-Accept or Re-request
… … …
N-negotiate and provide service
3-Provide Service or Offer alternative
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Negotiation Challenges Representation
Represent the request and offers Learning
Learn about consumer’s preferences based on requests and counter offers
Similarity Estimation Estimate similarity between the request and
available services Revision
Revise requests or offers based on incoming information
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Representation
The request of the consumer and the counter offer of the provider are represented as vectors.
Example domain Service: Wine Service features: winery, type of grape, sugar level,
flavor, body of the wine, color of the wine, region Example request or offer vector:
(Bancroft, ChardonnayGrape, Dry, Moderate, Medium, White, NapaRegion)
winery type of grape sugar level flavor body color region
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Learning Phase
Preferences: Relative importance degree of features of the service
Learn preferences over interactions:Requires incremental learning algorithms
Learn preferences as concept: Version Space as an inductive learning
techniqueDecision Trees
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Learning Phase: Version Space Maintain two extreme hypotheses sets
The most general hypotheses Initially every possible hypotheses is here As the consumer rejects offers, this set is specialized
The most specific hypotheses Initially empty As the consumer makes requests, her requests are
generalized and kept in this set
The goal: Obtain a single description
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Modified Version Space
To support to learn disjunctive concept E.g. (red and strong wine) OR (rose and
delicate wine)
Extend hypothesis language to support learning disjunctive concepts Specialize general set minimally General set involves all possible hypothesis. Generalize specific set minimally Specific set only includes positive samples.
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Strong Moderate Delicate
Decision Trees
FLAVOR
COLOR --
++ -- -- ++
COLOR
Red Rose Red RoseRed Rose Red Rose
Acceptable Service:
(Strong and Red)
OR
(Moderate and Rose)
Rejectable Service:
(Strong and Rose)
OR
(Moderate and Red)
OR
(Delicate)
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Offering Service Random Offering Service
Offering service considering only the current request (SCR)
Offering Service using Version Space (VS)
Offering Service using Modified Version Space (MVS)
Offering Service using Decision Trees (DT)
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Offering Service using MVS At the beginning, load all possible services (e.g.
wine products) to the service list After each request, train the MVS with request
as a positive sample If there is an exactly matched service, offer it Otherwise,
Filter the service list with the most general set Estimate the similarity of each services with the most
specific set of learning component Offer the most similar service
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Offering Service using DT
After each request, rebuild the decision tree
Remove the services from service list, which are classified as negative
Offer the most similar service to the all previous and current requests
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Tversky’s Similarity Measure Terms:
Common: number of matched attributes Different: number of unmatched attributes α and β: Weights—Here α is equal to β
Example: S1= ( Full, Strong, Red ) S2= (Full, Delicate, Rose) SMs1s2 = 1 / 3
α *(common)SMpq =
α *(common) + β* (difference)
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Architectural Setup
Implementation in Java Ontology language: OWL Ontology Reasoner:Jena2 Ontology
Shared ontology: modified version Wine ontology
Producer’s service ontology: “WineStock” extension of wine ontology
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Evaluating The Learning Phase
Criteria: Number of iterations for consensus Five systems are compared
Similarity with Modified Version Space (SMVS) System using Decision Trees (DT) Similarity with Version Space (SVS) Similarity with Current Request (SCR) Random Offering (Random)
Use five scenarios Run five times and take average of runs Inventory that contains 19 available services
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Evaluating The Learning Phase Cont. Scenario 1:
Preference of consumer: Any wine whose sugar level is dry Availability in producer’s inventory: 15 products
Scenario 2: Preference of consumer: Any wine, which is red and dry Availability in producer’s inventory: Eight products
Scenario 3: Preference of consumer: Any wine, which is red ,dry and moderate Availability in producer’s inventory: Four products
Scenario 4: Preference of consumer: Any wine, which is strong and red Availability in producer’s inventory: Two products
Scenario 5: Preference of consumer: Any wine whose flavor is strong and color
is red or rose Availability in producer’s inventory: Three products
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Evaluating The Learning Phase Cont.
Average number of iterations for five scenarios SMVS SCR Random
Offering
SVS DT
Scenario-1: 1.21.2 1.41.4 1.21.2 1.21.2 1.21.2
Scenario-2: 1.41.4 1.41.4 2.62.6 1.41.4 1.41.4
Scenario-3: 1.41.4 1.81.8 4.44.4 1.41.4 1.41.4
Scenario-4: 2.22.2 2.82.8 9.69.6 1.81.8 22
Scenario-5: 22 2.62.6 7.67.6 1.75+No 1.75+No offeroffer
1.81.8
Average 1.64 2 5.08 1.51+No offer
1.56
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Similarity Measure
Tversky’s Similarity Measure
Proposed Semantic Similarity Measure (RP)
Resnik’s Semantic Similarity Measure
Lin’s Semantic Similarity Measure
Wu & Palmer’s Semantic Similarity Measure
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RP Semantic Similarity
Parent versus Grandparent Reddish Color is more similar
than WineColor to Rose
Parent versus Sibling WineColor is more similar than
ReddishColor to White
Sibling versus Grandparent Red is more similar than
WineColor to Rose
Thing
WineColor
WhiteReddishColor
Red Rose
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RP Semantic Similarity Cont.
Start the similarity with one at the node containing the first concept and decrease it by some constant at each level
Assume m is the constant for parents n is the constant for siblings
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RP Semantic Similarity Sample Rose-ReddishColor
1 * (2/3) = 0.67 Rose-Red
1 * (4/7) = 0.57 Rose-WineColor
1* (2/3)*(2/3) = 0.45 Rose-Thing
1*(2/3)*(2/3)*(2/3)= 0.30 Rose-White
1*(4/7)*(2/3) = 0.38 •Assume m=2/3 and n=4/7
Thing
WineColor
WhiteReddishColor
Red Rose
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Evaluating Similarity Metrics
Scenario 1-7 : use dataset1 (19 services) Scenario 8-10: use dataset2 (50 services) Scenario 6-10: consider the hierarchical
relation in preferences Sample scenario 9:
expensive red wine, which is located around California region or cheap white wine, which is located in around Texas region.
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Evaluating Similarity Metric Cont. Average number of iterations for ten scenarios
Tversky Resnik Lin Wu & Palmer RP
Scenario-1: 1.21.2 22 1.21.2 11 11
Scenario-2: 1.41.4 2.82.8 1.41.4 1.61.6 1.61.6
Scenario-3: 1.41.4 2.42.4 1.81.8 22 22
Scenario-4: 2.22.2 2.82.8 11 1.21.2 1.21.2
Scenario-5: 22 3.83.8 1.61.6 1.61.6 1.61.6
Scenario-6: 4.34.3 2.32.3 3.73.7 2.72.7 2.72.7
Scenario-7: 66 2.72.7 1.71.7 1.31.3 1.31.3
Scenario-8: 7.37.3 -- 2.72.7 2.72.7 33
Scenario-9: 6.76.7 -- 44 22 22
Scenario-10: 55 -- 2.72.7 2.72.7 2.32.3
Average 3.75 2.69 2.18 1.88 1.87
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General Results
Learning preferences shorten the negotiation duration
Usage of semantic similarity increases the performance when preferences are concerned
Using Modified Version Space or Decision Trees results in reasonable results.
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Contributions of thesis
A multi-issue negotiation mechanism based on the content of the service
Usage of ontologies so work with semantics
Extension of CEA Algorithm for disjunctive concepts
A new semantic similarity measure
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Future Work
Modeling producer’s preferences and business policyThe producer may prefer to provide some
services over others
Integration of learning with ontology reasoning
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