CONTENT-ORIENTED NEGOTIATION IN E-COMMERCE

Boğaziçi University Department of Computer Engineering

Reyhan Aydoğan

Thesis Advisor: Asst. Prof. Pınar Yolum

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OUTLINE

Negotiation Architecture Technical Details

Representation Learning Phase Similarity Estimation Offering Service Mechanism

Developed System & Performance Evaluation Discussion

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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