Discovering cohesive subgroups from social networks for targeted advertising

In this paper, we propose a framework that utilizes the concept of a social network for the targeted advertising of products. This approach discovers the cohesive subgroups from a customer's social network as derived from the customer's interaction data, and uses them to infer the probability of a customer preferring a product category from transaction records. This information is then used to construct a targeted advertising system. We evaluate the proposed approach by using both synthetic data and real-world data. The experimental results show that our approach does well at recommending relevant products.