TY - GEN
T1 - Stochastic coupon probing in social networks
AU - Tang, Shaojie
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/10/17
Y1 - 2018/10/17
N2 - CMO Council reports that 71% of internet users in the U.S. were influenced by coupons and discounts when making their purchase decisions. It has also been shown that offering coupons to a small fraction of users may affect the purchase decisions of many other users in a social network. This motivates us to study stochastic coupon probing problem in social networks. Assume there is a social network and a set of coupons. We can offer coupons to some users adaptively and those users who accept the offer will act as seeds and influence their friends in the social network. There are two constraints which are called the inner and outer constraints, respectively. The set of coupons redeemed by users must satisfy inner constraints, and the set of all probed users must satisfy outer constraints. One seeks to develop a coupon probing policy that achieves the maximum influence while satisfying both inner and outer constraints. Our main result is a constant approximation policy for the stochastic coupon probing problem for any monotone submodular utility function.
AB - CMO Council reports that 71% of internet users in the U.S. were influenced by coupons and discounts when making their purchase decisions. It has also been shown that offering coupons to a small fraction of users may affect the purchase decisions of many other users in a social network. This motivates us to study stochastic coupon probing problem in social networks. Assume there is a social network and a set of coupons. We can offer coupons to some users adaptively and those users who accept the offer will act as seeds and influence their friends in the social network. There are two constraints which are called the inner and outer constraints, respectively. The set of coupons redeemed by users must satisfy inner constraints, and the set of all probed users must satisfy outer constraints. One seeks to develop a coupon probing policy that achieves the maximum influence while satisfying both inner and outer constraints. Our main result is a constant approximation policy for the stochastic coupon probing problem for any monotone submodular utility function.
UR - https://www.scopus.com/pages/publications/85058026772
U2 - 10.1145/3269206.3271771
DO - 10.1145/3269206.3271771
M3 - Conference contribution
AN - SCOPUS:85058026772
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 1022
EP - 1031
BT - CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management
A2 - Paton, Norman
A2 - Candan, Selcuk
A2 - Wang, Haixun
A2 - Allan, James
A2 - Agrawal, Rakesh
A2 - Labrinidis, Alexandros
A2 - Cuzzocrea, Alfredo
A2 - Zaki, Mohammed
A2 - Srivastava, Divesh
A2 - Broder, Andrei
A2 - Schuster, Assaf
PB - Association for Computing Machinery
T2 - 27th ACM International Conference on Information and Knowledge Management, CIKM 2018
Y2 - 22 October 2018 through 26 October 2018
ER -