TY - GEN
T1 - ReducE-Comm
T2 - 28th ACM International Conference on Information and Knowledge Management, CIKM 2019
AU - Gershtein, Shay
AU - Milo, Tova
AU - Novgorodov, Slava
N1 - Publisher Copyright: © 2019 Association for Computing Machinery.
PY - 2019/11/3
Y1 - 2019/11/3
N2 - Many e-commerce platforms serve as an intermediary between companies/manufacturers and consumers, receiving a commission per purchase. To increase revenue, such sites tend to offer a wide variety of items. However, in many situations a smaller subset of the items should be selected and offered for sale, e.g., when opening an express branch or expanding to a new region, or when maintenance costs become prohibitive and redundant items should be disposed of. In all these cases selecting a reduced inventory which covers most consumer needs is an important goal. In this demo we introduce ReducE-Comm - a highly parallelizable and scalable system that given a large set of items, a bound on the number of items that can be supported and information about consumer preferences/items relationships, allows to select a subset of the items which maximizes the likelihood of a purchase. Our system is interactive and facilitates real-time analysis, by providing detailed per-item impact statistics. We demonstrate the effectiveness of ReducE-Comm on real-world data and scenarios taken from a large e-commerce system, by interacting with the CIKM'19 audience who act as analysts aiming to intelligently reduce the inventory.
AB - Many e-commerce platforms serve as an intermediary between companies/manufacturers and consumers, receiving a commission per purchase. To increase revenue, such sites tend to offer a wide variety of items. However, in many situations a smaller subset of the items should be selected and offered for sale, e.g., when opening an express branch or expanding to a new region, or when maintenance costs become prohibitive and redundant items should be disposed of. In all these cases selecting a reduced inventory which covers most consumer needs is an important goal. In this demo we introduce ReducE-Comm - a highly parallelizable and scalable system that given a large set of items, a bound on the number of items that can be supported and information about consumer preferences/items relationships, allows to select a subset of the items which maximizes the likelihood of a purchase. Our system is interactive and facilitates real-time analysis, by providing detailed per-item impact statistics. We demonstrate the effectiveness of ReducE-Comm on real-world data and scenarios taken from a large e-commerce system, by interacting with the CIKM'19 audience who act as analysts aiming to intelligently reduce the inventory.
UR - http://www.scopus.com/inward/record.url?scp=85075432573&partnerID=8YFLogxK
U2 - 10.1145/3357384.3357861
DO - 10.1145/3357384.3357861
M3 - منشور من مؤتمر
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 2957
EP - 2960
BT - CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
Y2 - 3 November 2019 through 7 November 2019
ER -