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CSC 177

CSC 177. Research Paper Review Chad Crowe. A Microeconomic Data Mining Problem: Customer-Oriented Catalog Segmentation. Authors: Martin Ester, Rong Ge, Wen Jin, Zengjian Hu Year of Publication: 2004 Simon Fraser University, Burnaby, B.C., Canada C.S. Department

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CSC 177

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  1. CSC 177 Research Paper Review Chad Crowe

  2. A Microeconomic Data Mining Problem:Customer-Oriented Catalog Segmentation • Authors: Martin Ester, Rong Ge, Wen Jin, Zengjian Hu • Year of Publication: 2004 • Simon Fraser University, Burnaby, B.C., Canada • C.S. Department • Author or Co-Author on 44 different publications since 1984 – mainly data mining subjects • Paper’s sponsors: • SIGMOD, SIGKDD, ACM • Paper available on ACM portal: portal:http://portal.acm.org/citation.cfm?doid=1014052.1014119

  3. Problem • Companies want to design k product catalogs of size r that maximize the overall number of catalog products purchased after having sent the best matching catalog to the customer. • There are situations where a customer who is interested in a company will purchase a product beyond the catalog.

  4. Problem

  5. Technical Summary • K-MECWT: the task is to find k catalogs maximizing the number of distinct customers who have at least t interesting products in the catalog that is sent to them. • Best-Fit Algorithm (greedy): constructs one catalog at a time by choosing the next product for that catalog based on a set quality criteria. • Very efficient • Returns a solution that is only locally optimal • Random-Product-Fit Algorithm: randomly replaces catalog products • Replacement can be done completely at random or from a deterministic approach.

  6. Methods Introduced • Using K-MECWT, Best-Fit algorithm, and Random-Product-Fit algorithm together • K-MECWT is used to find k clusters of customers where each cluster is described by a set of products and each customer is assigned to the cluster with the most similar cluster description. • Best-Fit algorithm then takes the data and assigns scores to each product based on the number of customers interested in the product and the number of additional products that these customers need to be interested in. • Random-Product-Fit algorithm, to overcome the lack of back-tracking, uses deterministic random product replacement.

  7. Opinion • Interesting topic • Marketing goods effectively is always a huge field of interest. • Their live and synthetic experiments show that this technique yielded more products sold.

  8. Customer-Oriented Catalog Segmentation • Create synthetic data, possibly using “IBM Quest Market-Basket Synthetic Data Generator” • Attempt to obtain live data. • We will evaluate the quality of our catalogs obtained by K-MECWT and algorithms: Best-Product-Fit and Random-Product-Fit and compare them to a traditional catalog segmentation technique in DCC(Direct Catalog Creation).

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