Abstract
Marketing through online social networks is convenient, low-cost, and beneficial for companies seeking to expand their customer numbers. In the literature, many studies address the influence maximization problem with one or multiple products, which selects initial consumers (seeds) to spread one or multiple product information such that the number of consumers receiving these product information (the influenced consumers) is maximized. However, to date, none of these schemes take the rumors and the beliefs of other persons that could significantly change the consumer's behavioral intention into account at once. In this paper, we fill this gap by proposing a new variant of the influence maximization problem with multiple products, the Budgeted Behavioral Intentions Maximization problem, which asks for a set of seeds with the total cost not greater than a given budget in online social networks such that the total expected behavioral intentions of the consumers influenced by the selected seeds and the rumors are maximized. In addition, we propose an approximation algorithm for the Budgeted Behavioral Intentions Maximization problem. We also conduct simulations to evaluate the performance of our algorithm using real traces and synthesis data. Experimental results show that our algorithm outperforms several greedy algorithms.