mcs thesis by: sébastien mathieu supervisors: dr. virendra c. bhavsar and dr. harold boley...
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3 Introduction (1/5) Internet as a market place Web portals –Simple portals ( )www.amazon.com –Match-making portals ( )www.telezoo.com –Bartering portals ( )www.tandcglobal.com –Advanced portal proposals ( )www.teclantic.caTRANSCRIPT
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MCS ThesisMCS Thesis
By: Sébastien Mathieu
Supervisors: Dr. Virendra C. Bhavsar and Dr. Harold BoleyExamining Board: Dr. John DeDourek, Dr. Weichang Du, Dr. Donglei Du
December 5th, 2005
Match-Making
in Bartering Scenarios
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AgendaAgenda
• Introduction• Background• Bartering Trees• Tree Approximation• Ring Bartering Algorithm• Computational Results• Conclusion
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Introduction (1/5)Introduction (1/5)
• Internet as a market place• Web portals
– Simple portals ( www.amazon.com )
– Match-making portals ( www.telezoo.com )
– Bartering portals ( www.tandcglobal.com )
– Advanced portal proposals ( www.teclantic.ca )
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Introduction (2/5)Introduction (2/5)
• Bartering
The practice of exchanging goods or services without using the medium of money [2]
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Introduction (3/5)Introduction (3/5)
• Bartering
Seek2Offer1
Seek1 Offer2
Agent1 Agent2Similarity1
Aggregate
Similarity
Similarity2
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Introduction (4/5)Introduction (4/5)
• Ring Bartering
Seek2Offer1
Seek1 Offer2
Agent1 Agent2
Similarity1
Offer3
Seek3
Agent3
Similarity4 >> Similarity2
Similarity3 >> Similarity2
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Introduction (5/5)Introduction (5/5)
• Ring Bartering
Agent1O S Agent2O S
AgentkO S
Agentn-1O S
AgentnO S
…
…
s1
s2
sk-1
sk
sn-2
sn-1
sn
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Background (1/4)Background (1/4)
• Different match-making techniques– IBM Websphere rules and properties– Agent-Mediated eCommerce System with
Decision Analysis Features [15]
– Bhavsar/Boley/Yang Tree similarity algorithm [1,11,12,15,16]
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Background (2/4)Background (2/4)
• Arc labelled weighted trees
• Labels on Nodes, fanout-unique labels on Arcs
• Relative importance on Arcs weights ( Σwi = 1.0)
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Background (3/4)Background (3/4)
• Similarity Algorithm– Computes the similarity between two arc
labeled weighted trees– Top-down traversal / Bottom-up computation– Can handle trees having different arc labels
and structures
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Background (4/4)Background (4/4)
• Different bartering approaches
– The Trade Balance Problem [12]
– Multi-Agent Learning Improvement [20]
– Ring Bartering in P2P [3]
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Bartering Trees (1/3)Bartering Trees (1/3)
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Bartering Trees (2/3)Bartering Trees (2/3)
• Computing the Aggregate SimilarityArithmetic mean not judicious
E.g.: Similarity ( Offer1, Seek2 ) = 1.0 Similarity ( Seek1, Offer2 ) = 0.0
Aggregate similarity = 0.5?
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Bartering Trees (2/3)Bartering Trees (2/3)
• Computing the Aggregate SimilarityArithmetic mean not judicious
E.g.: Similarity ( Offer1, Seek2 ) = 1.0 Similarity ( Seek1, Offer2 ) = 0.0
Aggregate similarity = 0.5? Aggregate similarity ~ 0.3=
( Aggregate similarity reasonably less than 0.5)
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Bartering Trees (3/3)Bartering Trees (3/3)
The Aggregation Function with a = -1.5
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Tree Approximation (1/3) Tree Approximation (1/3)
• Motivations– To represent our Trees in a multi-dimensional space and
use spatial data-structures– To avoid the computation of all similarity values
• Concepts– Base: Set of Trees formed by all possible unary trees
The maximum depth is the level of the base The lower the level, the greater the approximation
– Dimension: Number of Trees in the base
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Tree Approximation (1/3) Tree Approximation (1/3)
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Tree Approximation (2/3)Tree Approximation (2/3)
• Notion of Distance
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Tree Approximation (3/3)Tree Approximation (3/3)
• Behavior of Distance against Similarity
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Notion of RiskNotion of Risk
• The risk takes into account:– The number of participants in the trade– The similarities between the corresponding seeks and
offers that are involved in the trade
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Ring Bartering Algorithm (1/6)Ring Bartering Algorithm (1/6)
• Our algorithm – Returns the (finite) set of rings starting from a given
agent
• Divided into three main phases:– Repeated selection of the closest Offers (for a given
Seek) first pruning step– Closure of the ring– Testing of the risk second pruning step
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Ring Bartering Algorithm (2/6)Ring Bartering Algorithm (2/6)
• Overall Algorithm
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Ring Bartering Algorithm (3/6)Ring Bartering Algorithm (3/6)
• Selection of the closest Offers
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Ring Bartering Algorithm (4/6)Ring Bartering Algorithm (4/6)
• Closure of the ring
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Ring Bartering Algorithm (5/6)Ring Bartering Algorithm (5/6)
• Testing of the risk
• Ideal Agent = Agent having similarity equal to one with both the previous and the following agent in the ring
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Ring Bartering Algorithm (6/6)Ring Bartering Algorithm (6/6)
• Properties of our algorithm
– A ring starting from an Agentj of the agent database will be reported by the algorithm, called with Agentj as argument, if and only if it is Dmax/Rmax acceptable
– Suppose a ring is reported by the algorithm when starting with a given agent. This ring, will be also reported if we start the algorithm with any of the other agents in the ring
Dmax = Maximum DistanceRmax = Maximum RiskDmax/Rmaxacceptable = Risk below Rmax, all Distances below Dmax
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Computational Results (1/4)Computational Results (1/4)
• Influence of the Distance
• Highest Missing Ring = Similarity of the first missing ring when sorted by aggregate similarity
• Number of Highest non Missing Rings = Number of Rings before the first missing ring when sorted by aggregate similarity
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Computational Results (2/4)Computational Results (2/4)
• Influence of the Risk
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Computational Results (3/4)Computational Results (3/4)
• Computation Time and Size of the Rings
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Computational Results (4/4)Computational Results (4/4)
• Computation Time without Pruning (ie Dmax = ∞ and Rmax = 1)
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Conclusion (1/2)Conclusion (1/2)
• We moved from the restrictive buyer/seller scenario to bartering and ring bartering scenarios
• We developed an efficient algorithm using two pruning techniques based on the notions of Distance and Risk
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Conclusion (2/2)Conclusion (2/2)
• Future Work– Pairing: to create the best combination of rings
involving every agent in the virtual market place exactly once
– Local Similarity: can improve our tree approximation by adding information without increasing the number of dimensions
– Transfer tree approximation technique back to indexing in non-bartering scenario
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Questions ?Questions ?
Thanks !
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• A zero Distance example with a low similarity for a level 1 base
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• Seller weights: an example
Seller1 emphasizes his/her pool easier negotiation phase
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• An example of Base
Bases of dimension 5 and 2