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Project and Product Selection. by He Jiang Department of Management University of Utah April 1 st , 2003. Outline. On Integrating Catalogs A Hierarchical Constraint Satisfaction Approach to Product Selection for Electronic Shopping Support

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Project and Product Selection

by

He Jiang

Department of Management

University of Utah

April 1st, 2003


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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


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On Integrating Catalogs

Rakesh Agrawal and Ramakrishnan Srikant

IBM Almaden Research Center


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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


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Problem—Why 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


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Problem Identification—Model 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.


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Question

How to Integrate?


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Straightforward Approach:

  • Completely ignore N’s categorization, put each of N’s product into M’s category according to M’s classification rule.


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Enhanced Approach

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


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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 M>N, can help distinguish categories that M doesn’t have; if N>M, NBHC can be applied.


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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.





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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.


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Experimental Results—Data 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;





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Experimental Results—Catalog Size


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Experimental Results—Catalog Size


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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


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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


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Summary Selection for Electronic Shopping Support

  • 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.


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Simple taxonomy Hierarchy Approach Selection for Electronic Shopping Support


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Question Selection for Electronic Shopping Support

  • 1. How do we search for a sugar-free decaffeinated cola?

  • 2. If there isn’t a cola that satisfy all the requirements, i.e., cola, sugar-free and decaffeinated. what’s your recommendation?


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Gaps Selection for Electronic Shopping Support

  • 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.


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Hierarchical Constraint Satisfaction Approach Selection for Electronic Shopping Support

  • 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 isn’t a solution that is consistent with given constraints due to conflicting constraints.


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Concepts Introduced Selection for Electronic Shopping Support

  • 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;


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Constraint Satisfaction Error Selection for Electronic Shopping Support

  • 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.


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Hierarchical reasoning and indifference interval Selection for Electronic Shopping Support


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Constraint Hierarchies Selection for Electronic Shopping Support


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Example Selection for Electronic Shopping Support

  • 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”.


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Example Selection for Electronic Shopping Support—Result


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Contributions and Future Research Directions Selection for Electronic Shopping Support

  • 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.


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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


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Summary Selection

  • 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 (R&S) 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


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Gaps Selection

  • Most of the R&S 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 doesn’t 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.


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Assumptions Selection

  • Decision maker’s 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;





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Additive MAU Model Selection

  • If mutual utility additive independent, then

  • Example for additive independence:


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Single Attribute Utility Function Used Selection

  • Methods for assigning weights: trade-off method; analytical hierarchy process (AHP).


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Question Selection

  • What’s the benefit of using this function?


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R&S Experimental Set-up Selection

  • Correct Selection (CS): the R&S procedure accurately identifies the configuration with largest expected utility .

  • Two stage indifference zone procedure for R&S.


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Selection of Selection

  • A Utility Exchange Approach

    Table 1 Alternatives by Measures Matrix for Car Selection

    Table 2 Equivalent Hypothetical Cars


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Question Again Selection

  • Does it mean that the 20 horsepower is worth $1,200?


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Selection of Selection


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Establishing the Indifference Zone Selection

  • Curve dividing the indifference and preference zone:


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Application of the Procedure—Case Description Selection

  • Case example: Land Seismic Survey;

  • Performance measures: survey cost; survey duration; utilization of the four crews;

  • Relationship of the crews:





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Contributions and Future Research Directions Weight

  • 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 Inoue’s work to include their Bayesian technique and relieve some of the computational burden of all R&S procedure; combine the work in this paper with R&S 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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