C ONTENT-ORIENTED NEGOTIATION IN E-C OMMERCE - PowerPoint PPT Presentation

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C ONTENT-ORIENTED NEGOTIATION IN E-C OMMERCE

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  1. Boğaziçi University Department of Computer Engineering CONTENT-ORIENTED NEGOTIATION IN E-COMMERCE 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. ConsumerAgent Producer Agent Negotiation Architecture Data Repository (Inventory Information) ? ? 4-Evaluatethe offer 1- Request 2-EvaluateRequest and Learning 3-Provide Service or Offer alternative 5-Accept or Re-request … … … <Preferences> <price v=low/> <speed v=high/> …………… </Preferences> SHARED ONTOLOGY N-negotiate and provide service

  4. 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

  5. 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

  6. 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 technique • Decision Trees

  7. 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

  8. 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.

  9. Decision Trees FLAVOR Acceptable Service: (Strong and Red) OR (Moderate and Rose) Strong Moderate Delicate COLOR COLOR - Rejectable Service: (Strong and Rose) OR (Moderate and Red) OR (Delicate) Red Rose Red Rose + - - +

  10. 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)

  11. 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

  12. 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

  13. α *(common) • SMpq = • α *(common) + β* (difference) 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

  14. 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

  15. 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

  16. 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

  17. Evaluating The Learning Phase Cont. • Average number of iterations for five scenarios

  18. 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

  19. Thing WineColor ReddishColor White Red Rose 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

  20. 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

  21. Thing WineColor ReddishColor White Red Rose 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

  22. 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.

  23. Evaluating Similarity Metric Cont. • Average number of iterations for ten scenarios

  24. 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.

  25. 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

  26. Future Work • Modeling producer’s preferences and business policy • The producer may prefer to provide some services over others • Integration of learning with ontology reasoning