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Similarity Evaluation Techniques for Filtering Problems. ?. Vagan Terziyan University of Jyvaskyla vagan@it.jyu.fi. Evaluating Distance between Various Domain Objects and Concepts - one of the basic abilities of an intelligent agent. Are these two the same?. … No !

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similarity evaluation techniques for filtering problems

Similarity Evaluation Techniques for Filtering Problems

?

Vagan Terziyan

University of Jyvaskyla

vagan@it.jyu.fi

slide2

Evaluating Distance between Various Domain Objects and Concepts - one of the basic abilities of an intelligent agent

Are these two the same?

… No !

The difference is equal to 0.234

contents
Contents
  • Goal
  • Basic Concepts
  • External Similarity Evaluation
  • An Example
  • Internal Similarity Evaluation
  • Conclusions
reference
Reference

Puuronen S., Terziyan V., A Similarity Evaluation Technique for Data Mining with an Ensemble of Classifiers, In: A.M. Tjoa, R.R. Wagner and A. Al-Zobaidie (Eds.), Proc. of the 11th Intern. Workshop on Database and Expert Systems Applications, IEEE CS Press, Los Alamitos, California, 2000, pp. 1155-1159.

http://dlib.computer.org/conferen/dexa/0680/pdf/06801155.pdf

slide5
Goal
  • The goal of this research is to develop simple similarity evaluation technique to be used for social filtering
  • Result of social filtering here here is prediction of a customer’s evaluation of certain product based on known opinions about this product from other customers
basic concepts virtual training environment vte
Basic Concepts:Virtual Training Environment (VTE)
  • VTEis a quadruple:

<D,C,S,P>

      • Dis the set of goods D1, D2,..., Dn in the VTE;
      • C is the set of evaluation marks C1, C2,..., Cm ,that are used to rank the products;
      • Sis the set of customers S1, S2,..., Sr , who select evaluation marks to rank the products;
      • Pis the set of semantic predicates that define relationships between D, C, S
external similarity values
External Similarity Values

External Similarity Values (ESV): binary relations DC, SC, and SD between the elements of (sub)sets of D and C; S and C; and S and D.

ESV are based on total support among all the customers for voting for the appropriate connection (or refusal to vote)

internal similarity values
Internal Similarity Values

Internal Similarity Values (ISV): binary relations between two subsets of D, two subsets of C and two subsets of S.

ISV are based on total support among all the customers for voting for the appropriate connection (or refusal to vote)

why we need similarity values or distance measure
Why we Need Similarity Values (or Distance Measure) ?
  • Distance between products is used to advertise the customers a new product based on evaluation of already known similar products
  • distance between evaluations is necessary to estimate evaluation error when necessary, e.g. in the case of adaptive filtering technologies used
  • distance between customers is useful to evaluate weights of all customers when necessary, e.g. to be able to integrate their opinions by weighted voting.
deriving external relation dc how well evaluation fits the product
Deriving External Relation DC:How well evaluation fits the product

Evaluation marks

Products

Customers

deriving external relation sc measures customer s competence in the use of evaluation marks
Deriving External Relation SC:Measures customer’s competence in the use of evaluation marks
  • The value of the relation (Sk,Cj) in a way represents the total support that the customer Sk obtains selecting (refusing to select) the mark Cj to evaluate all the products.
example of sc relation
Example of SC Relation

Evaluation marks

Products

Customers

deriving external relation sd measures customer s competence in the products
Deriving External Relation SD:Measures customer’s competence in the products
  • The value of the relation (Sk,Di) represents the total support that the agent Sk receives selecting (or refusing to select) all the solutions to solve the problem Di.
example of sd relation
Example of SD Relation

Products

Evaluation marks

Customers

normalizing external relations to the interval 0 1
Normalizing External Relations to the Interval [0,1]

nis the number of products

mis the number of evaluation marks

ris the number of customers

slide19

Competence of a customer

Evaluation marks

Goods

Conceptual pattern of evaluation marks definitions

Cj

Conceptual pattern of goods’ features

Di

Competence in the goods

Competence in the evaluation marks

Customer

quality balance theorem
Quality Balance Theorem

The evaluation of a customer’s competence (ranking, weighting, quality evaluation) does not depend on the competence area “virtual world of products” or “conceptual world of evaluation marks” because both competence values are always equal.

proof
Proof

...

...

an example
An Example
  • Let us suppose that four customers have to evaluate three products from virtual shop using five different evaluation marks available.
  • The customers should define their selection of appropriate mark for every product.
  • The final goal is to obtain a cooperative evaluation result of all the customers concerning the quality of products.
c set evaluation marks in the example
C set (evaluation marks) in the Example

Evaluation marks Notation

Nicely designed C1

Expensive C2

Easy to use C3

Reliable C4

Safe C5

s customers set in the example
S (customers) Set in the Example

Customers IDs Notation

Fox S1

Wolf S2

Cat S3

Hare S4

d products set in the example
D (products) Set in the Example

D1 - Ultra Cast Spinning Reel

D2 - Nokia Communicator 9110

D3 - iGrafx Process Management Software

evaluations made for the good reel
Evaluations Made for the Good“Reel”

D1

P(D,C,S) C1 C2 C3 C4 C5

S11 -1 -1 0 -1

S20+ -1** 0 ++ 1* -1***

S30 0 -1 1 0

S41 -1 0 0 1

Customer Wolf prefers to select mark Reliable*to evaluate “Reel” and it refuses to select Expensive** or Safe***. Wolf does not use or refuse to use the Nicely designed+or Easy to use++ marks for evaluation.

evaluations made for the good communicator
Evaluations Made for the Good“Communicator”

D2

P C1 C2 C3 C4 C5

S1-1 0 -1 0 1

S21 -1 -1 0 0

S31 -1 0 1 1

S4-1 0 0 1 0

evaluations made for the good software
Evaluations Made for the Good“Software”

D3

P C1 C2 C3 C4 C5

S11 0 1 -1 0

S20 1 0 -1 1

S3-1 -1 1 -1 1

S4-1 -1 1 -1 1

example calculating value dc 3 4
Example: Calculating Value DC3,4

D3

P C1 C2 C3C4 C5

S11 0 1 -1 0

S20 1 0 -1 1

S3-1 -1 1 -1 1

S4-1 -1 1 -1 1

slide34

Result of Cooperative Goods Evaluation Based on DC Relation

D1 is nicely designed, reliable, not expensive, but not easy to use

D2 is reliable, safe, not expensive, but not easy to use

D3 is easy to use, safe, but not reliable

normalized and thresholded sd relation
Normalized and “Thresholded” SD relation

Fox

Wolf

Cat

Hare

Evaluations obtained from the

customer Fox should be accepted if he

evaluates goods similar to “Reels” ...

… or similar to “Software” .

Fox’s evaluations should be rejected if

they concern goods similar to “Communicator”

slide39

Normalized and “Thresholded” SD relation

Fox

Wolf

Cat

Hare

Only evaluation from the customer

Cat can be accepted if it concerns

goods similar to “Communicator”

All four customers are expected

to give an acceptable evaluations

concerning “Software” related goods

normalized and thresholded sc relation
Normalized and “Thresholded” SC relation

Nicely designed

Easy to use

Expensive

Safe

Reliable

Fox

Wolf

Cat

Hare

Evaluation obtained from the customer

Fox should be accepted if it concern

usability (easy to use) of a good...

Fox’s evaluations

should be rejected

if they concern

design of goods

… or reliability of a good .

slide42

Internal Similarity Values

Internal Similarity Values (ISV): binary relations between two subsets of D, two subsets of C and two subsets of S.

ISV are based on total support among all the customers for voting for the appropriate connection (or refusal to vote)

deriving internal similarity values
Deriving Internal Similarity Values

Via one intermediate set

Via two intermediate sets

internal similarity for customers evaluation marks goods based similarity
Internal Similarity for Customers:Evaluation marks-Goods-Based Similarity

Goods

Evaluation marks

Customers

internal similarity for evaluation marks
Internal Similarity for Evaluation Marks

Goods-based similarity

Customers-based similarity

Goods-customers-based similarity

internal similarity for goods
Internal Similarity for Goods

Evaluation marks-based similarity

Customers-based similarity

Evaluation marks-customers-based similarity

conclusion
Conclusion
  • Discussion was given to methods of deriving the total support of each binary similarity relation. This can be used, for example, to derive the most supported goods evaluation and to rank the customers according to their competence
  • We also discussed relations between elements taken from the same set: goods, evaluation marks, or customers. This can be used, for example, to divide customers into groups of similar competence relatively to the goods evaluation environment
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