Least Cost Precision Marketing Based on User Profiles in Social Networks

Abstract

With the booming development of online marketing in social networks, it is increasingly valuable to adopt precision marketing based on user profiles by analyzing users’ behaviors and attributes. In this paper, the Least Cost Precision Marketing problem based on User Profiles in social networks (LCPM-UP problem) is proposed. Its objective is to minimize the cost to choose initial users while at least J target users described by certain user profile are influenced. Considering that users have different propagation capabilities in social networks, a novel propagation model named Limited Diffusion Independent Cascade model (LD-IC model) is presented. It is proved that the LCPM-UP problem in LD-IC model is NP-hard and the influence propagation function is submodular and monotonically increasing. Therefore, a greedy algorithm is developed for the problem. However, the greedy algorithm is too time consuming to be scalable to large networks, so the Target User Local Influence Heuristic algorithm (TU-LIH) is proposed by utilizing local influences of each node to approximate the influence propagation in LD-IC model. Extensive experiments on datasets from four real social networks demonstrate the effectiveness and efficiency of proposed algorithms.