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Project and Product Selection

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Title: Project and Product Selection


1
Project and Product Selection
  • by
  • He Jiang
  • Department of Management
  • University of Utah
  • April 1st, 2003

2
Outline
  • On Integrating Catalogs
  • A Hierarchical Constraint Satisfaction Approach
    to Product Selection for Electronic Shopping
    Support
  • A Multiple Attribute Utility Theory Approach to
    Ranking and Selection

3
  • On Integrating Catalogs
  • Rakesh Agrawal and Ramakrishnan Srikant
  • IBM Almaden Research Center

4
Summary
  • Problem integrating documents from different
    sources into a master catalog.
  • Gaps Many data sources have their own
    categorizations implicit similarity information
    in these source catalogs may be ignored.
  • Approaches Naïve Bayes classification
  • Contribution classification accuracy can be
    improved by incorporate the implicit similarity
    information present in these source
    categorizations

5
ProblemWhy Integration?
  • B2C shops need to integrate catalogs from
    multiple vendors ( Amazon)
  • B2B portals merged into one company (Chipcenter
    Questlink ?eChips)
  • Information portals categorize documents into
    categories (Google Yahoo!).
  • Corporate portals Merge intra-company and
    external information into a uniform categorization

6
Problem IdentificationModel Building
  • Problem identification classification problem.
  • Master catalog M with categories C1, C2, , Cn
  • Source catalog N with categories S1, S2, , Sm
  • Merge documents in N into M.

7
Question
  • How to Integrate?

8
Straightforward Approach
  • Completely ignore Ns categorization, put each of
    Ns product into Ms category according to Ms
    classification rule.

9
Enhanced Approach
  • incorporate the implicit categorization
    information present in N into M.

10
Assumptions and Limitations
  • M and N may are homogeneous and have significant
    overlap
  • M and N use the same vocabularies (Larkey, 1999).
  • Catalog hierarchies is flattened and is treated
    as a set of categories(Good 1965 Chakrabarti
    1997)
  • Different hierarchy levels (if MgtN, can help
    distinguish categories that M doesnt have if
    NgtM, NBHC can be applied.

11
Related Works and Gaps
  • Naïve-Bayes classifiers are accurate and
    fast(Chakrabarti et al 1997, ), so we choose
    Bayesian model
  • Folder systems such as email routing(Agrawal et
    al, 2000,), action predicting(Maes, 1994
    Payne et al, 1997), query organizing using text
    clustering(Sahami et al, 1998) and filings
    transferring(Dolin et al 1999) But none of this
    systems address the task of merging hierarchies
  • The Athena system includes the facility of
    reorganizing folder hierarchy into a new
    hierarchy (Agrawal et al, 2000) But no
    information from the old hierarchy is used in
    either building the model or routing the
    documents.

12
Straightforward Approach

13

Straightforward ApproachContinued
14
Enhanced Bayes Classification
15
Effect of Weight on Accuracy
  • Weight can make difference for a given M and N
    Tune set method to select a good value for the
    weight.
  • in which the document will be correctly
    classified or will never be correctly classified
  • The highest possible accuracy achievable with the
    enhanced algorithm is no worse than what can be
    achieved with the basic algorithm.

16
Experimental ResultsData Sets Used
  • Synthetic catalog deriving source catalog N from
    M using different distributions(e.g. Gaussian).
  • Real Catalog two real-world catalogs that have
    some common documents treat the first catalog
    minus the common documents as M, the remaining
    documents in the second catalog as N

17
Experimental Results

18
Experimental Results

19
Experimental Results

20
Experimental ResultsCatalog Size
21
Experimental ResultsCatalog Size
22
Contributions and Future Research Directions
  • Contributions enhancing the standard Naive Bayes
    classification by incorporating the category
    information of the source catalogs the highest
    accuracy of the enhanced technique can be no
    worse than that can be achieved by standard Naïve
    Bayes classification.
  • Future research using other classifiers such as
    SVM to incorporating the implicit information of
    N requires further work

23
  • A Hierarchical Constraint Satisfaction Approach
    to Product Selection for Electronic Shopping
    Support
  • Young U. Ryu
  • IEEE Transactions on Systems, Man, and
    Cybernetics-Part A Systems and humans
  • Vol. 29, No. 6, November 1999

24
Summary
  • Problem proposing a product selection mechanism
    for electronic shopping support
  • Approach hierarchical constraint satisfaction
    (HCS) approach
  • Gap simple taxonomy hierarchy(STH) approach is
    flawed in that the the search is conducted on a
    single generic product hierarchy
  • HCS is more powerful and flexible than STH.

25
Simple taxonomy Hierarchy Approach

26
Question
  • 1. How do we search for a sugar-free
    decaffeinated cola?
  • 2. If there isnt a cola that satisfy all the
    requirements, i.e., cola, sugar-free and
    decaffeinated. whats your recommendation?

27
Gaps
  • Search is conducted on a single generic product
    hierarchy
  • There may exist a product that cannot satisfy all
    the constraints
  • A product may be evaluated to be better than
    another while there is no big differences between
    these two products.

28
Hierarchical Constraint Satisfaction Approach
  • Constraint Satisfaction a methodology
    determining assignments of values to variables
    that are consistent with given constraint
  • Hierarchical Constraint Satisfaction an
    extension of STH which minimizes the the
    satisfaction errors of hierarchically organized
    constraints based on their importance
  • Value of HCS can be applied to cases in which
    there isnt a solution that is consistent with
    given constraints due to conflicting constraints.

29
Concepts Introduced
  • Constraint domain transformation transformation
    of a Boolean constraint to a arithmetic
    constraint
  • Tree domain is one whose elements are structured
    as a tree thus can be handled more flexibly
  • Indifference interval overcome a shortcoming of
    hierarchical reasoning when the difference
    between two alternatives is small

30
Constraint Satisfaction Error
  • Measures the degree of satisfaction of an
    arithmetic constrain c by the constraint
    satisfaction error function
  • for Boolean constraint, transform them into
    arithmetic constraints
  • e.g.

31
Hierarchical reasoning and indifference interval
32
Constraint Hierarchies

33
Example
  • Shopping for wipes products using hierarchical
    constraint satisfaction approach. Each product is
    described by the following attributes
  • Cost cents per sheet
  • Add-on materials baking soda, aloe vera,
  • Strength measured by pressure(psi) that breaks a
    sheet
  • Dispenser type box, pop-up
  • Added artificial scent unscented, natural aloe
    scented, natural jasmine scented and chemical
    perfume scented
  • Product purpose general purpose, diaper
    change.

34
ExampleResult

35
Contributions and Future Research Directions
  • Contribution the product search mechanism is
    viewed as a satisfaction problem of
    hierarchically organized constraints over product
    attributes, thus it is more powerful and flexible
    than product selection based on a single product
    taxonomy hierarchy.
  • Future research Purchasing requirement
    specification or constraint hierarchy
    elicitation complete prototype implementation of
    the HCS approach actual purchasing/sales
    transaction based on speech act theory,
    illocutionary logic and inter-organizational
    activity coordination.

36
  • A Multiple Attribute Utility Theory Approach to
    ranking and Selection
  • John Butler, Douglas J. Morrice and
  • Peter W. Mullarkey
  • Management Science, Vol. 47, No. 6, June 2001

37
Summary
  • Problem developing a ranking and selection
    procedure for making comparison of systems that
    have multiple performance measures
  • Approach combining Multiple Attribute Utility
    Theory (MAUT) and statistical ranking and
    selection (RS) using indifference zone
  • Gaps costing approach is flawed in that accurate
    cost data may not be available, and it may be
    difficult to measure performance using costs..
  • Advantages rigorous close to business practice
    simpler to implement can estimate the number of
    simulations required can assess the relative
    importance of criteria

38
Gaps
  • Most of the RS literature focused on procedures
    that reduce the multivariate performance measures
    to a scalar performances measure problem, but
    these procedures may have some disadvantages,
    e.g. accurate cost data may not be available it
    maybe difficult to accurately attach a dollar
    value to intangible variables
  • Current techniques may require a complicated step
    of estimating a covariance matrix(Gupta
    Panchapakesan 1979)
  • Previous work doesnt provide an approach to
    estimate the number of simulations required to
    select the best configurations with a high level
    of probability(Andijani 1998, Kim Lin 1999).
  • Previous work lacks a trade-off mechanism that
    allows the decision maker to combine disparate
    performance measures.

39
Assumptions
  • Decision makers preferences are accurately
    represented ( Clemen 1991, Keeney Raiffa 1976)
  • Performance measures that is converted to utils
    can be converted to meaningful unit by choosing
    an invertible utility function
  • There is a indifference zone for the decision
    maker on all the performance measures

40
General Outline of the Procedure

41
Multilinear Utility Function

42
Multiplicative MAU Model

43
Additive MAU Model
  • If mutual utility additive independent, then
  • Example for additive independence

44
Single Attribute Utility Function Used
  • Methods for assigning weights trade-off method
    analytical hierarchy process (AHP).

45
Question
  • Whats the benefit of using this function?

46
RS Experimental Set-up
  • Correct Selection (CS) the RS procedure
    accurately identifies the configuration with
    largest expected utility .
  • Two stage indifference zone procedure for RS.

47
Selection of
  • A Utility Exchange Approach
  • Table 1 Alternatives by Measures Matrix for Car
    Selection
  • Table 2 Equivalent
    Hypothetical Cars

48
Question Again
  • Does it mean that the 20 horsepower is worth
    1,200?

49
Selection of

50
Establishing the Indifference Zone
  • Curve dividing the indifference and preference
    zone

51
(No Transcript)
52
  • Example

53
Application of the ProcedureCase Description
  • Case example Land Seismic Survey
  • Performance measures survey cost survey
    duration utilization of the four crews
  • Relationship of the crews

54
Application of the ProcedureResults

55
Application of the ProcedureResults
56
Application of the ProcedureSensitivity Analysis
to Weight

57
Contributions and Future Research Directions
  • Contribution provides a formal procedure that
    can be applied to realistic problems presents a
    scalar performance measure that can summarize
    performance on multiple criteria, including
    nonlinear preference functions and the relative
    importance of the measures
  • Future research combine MAU theory with the work
    of Chen et al extend the MAU methodology with
    Chick and Inoues work to include their Bayesian
    technique and relieve some of the computational
    burden of all RS procedure combine the work in
    this paper with RS procedures designed
    facilitate variance reduction through the use of
    common random numbers (See Matejcik and Nelson
    1995 and Goldman and Nelson 1998).
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