Viral marketing, a marketing strategy that leverages the influence power in intimate relationship, has become more prevalent due to the popularity of online social networking services in recent years. Consumers are more likely to make a purchase based on social media referrals. Since marketing through social media and traditional channels may target on different audiences, how to maximize the revenue of a telecommunications company by employing different advertising ways and selecting initial users for advertisements is a critical problem. Therefore, in this paper, we formulate a new research problem, namely Cost-Aware Multi-wAy Influence maXimization (CAMAIX) to address the need mentioned above. We design a 1/2-approximation algorithm with various pruning and budget allocation strategies to solve CAMAIX efficiently. We conduct extensive experiments on a large-scale real dataset from a telecommunications company. The results show that our proposed algorithm outperforms the baseline algorithms in both solution quality and efficiency.
Relation:
2015 IEEE International Conference on Big Data (Big Data) (2015), p.1306-1310
