Call For Papers

                      E-Commerce and Data Mining

                     Special issue of the journal
                 Data Mining and Knowledge Discovery

            Guest editors: Ronny Kohavi and Foster Provost

Will electronic commerce be the killer app for data mining?  There are
good arguments that it may.  E-commerce sites collect massive amounts
of data on customer purchases, browsing patterns, usage times, and
preferences.  Sites also can collect information on competitors'
offerings and prices.  They can adjust their assortments, prices, and
promotions quickly and dynamically, based on changing trends and
personalization rules.  Because e-businesses implement closed-loop
computerized solutions, many of the traditional barriers to the
effective application of data mining are significantly lower, such as
access to data, data transformations, process automation, and
timeliness of discoveries.

As our understanding of data mining has improved, the core
technologies are being deployed with specific goals in mind, and often
as components of larger systems.  We are moving along the technology
adoption lifecycle, crossing Moore's "chasm" from the early adopters
to the early majority.  With this shift, solutions showing clear
return on investment (ROI) now become critical.

This special issue of the journal Data Mining and Knowledge Discovery
is dedicated to data mining, knowledge discovery, and e-business.

                --------------- Scope ---------------

We solicit high-quality, original papers describing applications of
data mining and knowledge discovery to electronic commerce and
e-business, as well as applied and fundamental research addressing
data mining issues particular to these areas.  In all cases, the
papers should describe clearly the contributions to the field, how the
paper supports these contributions, and the relationships to existing
work.  For applications papers, contributions should include a clear
description of the problem, evidence of significant ROI or important
new capabilities (as much as possible), and lessons learned with
potential generalizations.

All areas of electronic commerce are relevant.  Particular problems of
interest include, but are not limited to: personalization (both model
discovery and deployment), mass customization, increasing market
basket value (e.g., cross-selling), improving customer satisfaction
and loyalty, improving search facilities, recommender systems (e.g.,
collaborative filtering), improving navigation, improving marketing,
improving advertising (e.g., ad matching and profiling), increasing
frequency of visits and conversion rates, reducing costs,
business-to-consumer and business-to-business transactions,
competitive intelligence, shopping agents, and the transfer of mined
knowledge to conventional stores and conventional distribution
channels (e.g., direct channels, self-service channels, indirect
channels).

Also relevant are general technical issues when applied to e-commerce.
These include, but are not limited to: integration with larger
e-commerce systems and data warehouses, incorporating performance
feedback (e.g., campaign management) to improve models, data
transformations (e.g., creation of customer signatures and profiles),
multi-level data (e.g., hierarchical data), text mining, clickstream
mining (e.g., web log analysis and abstractions), integration with
syndicated data, incorporating prior business knowledge,
post-processing operations (e.g., visualization and workflow
integration), privacy issues, and emerging standards (e.g., APIs).

Each paper should describe the following (when relevant): 

    - The e-commerce application and the need for data mining.  Can the
      formulation be abstracted?  How significant is the problem? 
    - Who are the users of the techniques?  Of the learned knowledge? 
    - Who "paid" for the work? 
    - How are success and ROI measured? 
    - What was the size of the data?  Was it limited (e.g., was sampling
      used)? 
    - How were the raw data transformed into formats suitable for existing
      algorithms?  What processes were required?  Why were certain
      transformations done?  Were others tried?  Examples: data cleaning,
      missing values, data rollup/aggregrations, hierarchical abstraction,
      customer signature generation, denormalization, and feature
      construction. 
    - Why were specific algorithms chosen, and which others were tried? 
    - What role did background knowledge play and how has it affected the
      process? 
    - What were the post-processing operations?  How were the results
      explained to users (visualizations, dimensionality reduction,
      explanations, what-if scenarios, sensitivity analyses)? 
    - How did the results affect the target users?  Is mining done 
      repeatedly, or was this a one-shot task? 

       --------------- Submission Requirements ---------------

Authors are encouraged to submit high quality, original work that
neither has appeared in, nor is under consideration by, other
journals. Submissions should be in 12pt font, 1.5 line-spacing, and
should not exceed 28 pages.  Shorter submissions, including technical
notes also are solicited.

Electronic submissions are required; postscript or Acrobat PDF will be
accepted.  Please follow the instructions given at:
http://www.research.microsoft.com/research/datamine/Ksub-e-instr.txt
and ftp your paper to ftp.research.microsoft.com You do not have to
submit hardcopies to Kluwer.  The editors will send hardcopies of all
papers in one package.  If you do not receive confirmation of receipt
three days after the deadline, please send e-mail to the editors
directly.

         --------------- Important Dates --------------------

                   Submission deadline: 16 Nov 1999
                 Acceptance notification: 22 Feb 2000


Please check http://robotics.stanford.edu/~ronnyk/ecommerce-dm/ for
more details and review criteria.  Authors are encouraged consider the
criteria when crafting their submissions. Specific questions and
clarifications should be sent to [EMAIL PROTECTED]





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