Stochastic Coupon Probing in Social Networks

In this paper, we 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

Seed Selection and Social Coupon Allocation for Redemption Maximization in Online Social Networks

Online social networks have become the medium for efficient viral marketing exploiting social influence in information diffusion. However, the emerging application Social Coupon (SC) incorporating social referral into coupons cannot be efficiently solved by previous researches which do not take into account the effect of SC allocation. The number of allocated SCs restricts the number of influenced friends for each user. In the paper, we investigate not only the seed selection problem but also the effect of SC allocation for optimizing the redemption rate which represents the efficiency of SC allocation. Accordingly, we formulate a problem named Seed Selection and SC allocation for Redemption Maximization (S3CRM) and prove the hardness of S3CRM. We design an effective algorithm with a performance guarantee, called Seed Selection and Social Coupon allocation algorithm. For S3CRM, we introduce the notion of marginal redemption to evaluate the efficiency of investment in seeds and SCs. Moreover, for a balanced investment, we develop a new graph structure called guaranteed path, to explore the opportunity to optimize the redemption rate. Finally, we perform a comprehensive evaluation on our proposed algorithm with various baselines. The results validate our ideas and show the effectiveness of the proposed algorithm over baselines.Comment: Full version (accepted by ICDE 2019