Modeling and utilizing dynamic influence strength for personalized promotion

As the social networking websites arise, the social network has become an important vehicle for sharing information and exerting influences. For the widespread utilization of social influences, a lot of works such as influence maximization and innovation promotion have been studied on various diffusion models. However, to the best of our knowledge, none of the existing works has incorporated the interplay between the intensity of interest and influence strength, which has been widely observed in social sciences, into the diffusion model. To fulfill this gap, in this paper, we propose the ID model that is able to capture the dynamic influence strength owing to the interplay. Under this ID model, we address the novel utilization of dynamic influence strength for personalized promotion to grow the intensity of a target individual's interest in an issue. In particular, to have the cost of promotion minimized, we introduce a novel Algorithm ISES to search for the least number of individuals as seeds in the promotion strategy. The ISES algorithm is able to identify the cost-effective solution by adopting the backtracking search and employing pruning strategies. On the real dataset of DBLP, the experiments demonstrate the effectiveness of ISES.