DSRE is a full-day workshop happening on May 9, 2020. This workshop welcomes submissions from both researchers and industry practitioners in Retail e-com. We solicit research works that are broadly related to data science on retail and e-commerce data, including data cleaning, data normalization, classification, clustering, predictive analytics, optimization techniques, and ranking. Submissions are solicited in the form of research papers which propose new techniques and advances using data mining techniques for Retail e-com, as well as industry papers that describe practical applications and system innovations in Retail e-com application areas. Case studies, works-in-progress and position and opinion papers are also welcome.
All papers should have a maximum length of nine pages (single-spaced, two column, 10 point font, and at least 1" margin on each side). Authors should use US Letter (8.5" x 11") paper size. Long papers are limited to 9 pages (including references) and short papers are limited to 6 pages (including references). Papers must have an abstract with a maximum of 300 words and a keyword list with no more than six keywords. Authors are required to submit their papers electronically in PDF format.
Papers must be prepared in LaTeX2e, and formatted using SIAM’s macro. The macro is available here. The filename is soda2e_061418.zip (LaTeX2e). Make sure you use the SIAM macro; papers prepared using other macros will not be accepted.
Submissions can describe work that is either (i) not previously published, (ii) recently accepted but not published, or (iii) summarization or expansion upon the previously published work with in last 6 months. All submissions must be in English. Please note that at least one of the authors of each accepted paper must register for the workshop and present the paper in-person.
All the papers are required to be submitted via EasyChair system (https://easychair.org/conferences/?conf=dsre2020workshop). For more questions about the workshop and submissions, please send email to email@example.com.