Call For Papers
E-Commerce and Data Mining
Special issue of the International 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
close-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 ecommerce-dm@cs.stanford.edu