کشف زیر گروه های منسجم شبکه های اجتماعی برای تبلیغات هدفمند

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.

The effectiveness of targeting a small portion of customers for advertising has long been recognized by businesses (Armstrong & Kotler, 1999) for two main reasons. First, the amount of product/service information available to customers is ever-increasing, and hence it is desirable to help customers wade through the information to find the product/service they want. Second, understanding the needs of current and potential customers is an essential part of customer-relationship management. The ability to accurately and efficiently identify the needs of customers and subsequently advertise products/services that they will find desirable will increase customer-retention, growth, and profitability of a business (Armstrong & Kotler, 1999). The traditional approach to targeted advertising is to (manually) analyze a historical database of previous transactions and the features associated with the (potential) customers, possibly with the help of some statistical tools, and identify a list of those customers who are most likely to respond to the advertisement of the product. The advent of new technologies has lead to automatic tools being advocated for identifying potential customers (Hayes, 1994), with many recommender systems having emerged over the past few years whose basic idea is to advertise products according to users’ preferences as obtained by ratings either explicitly stated by the users or implicitly inferred from previous transaction records, Web logs, or cookies.

In this paper, we have proposed a novel approach to the targeted advertising of products. This approach discovers cohesive subgroups in a social network that are used to identify a shortlist of prospective customers for a given product, for which we have developed a comprehensive algorithm. We have evaluated the proposed approach using both synthetic and real-world data. The results from applying this approach to email logs and library-circulation data have demonstrated the effectiveness of the proposed targeted advertising algorithms. The work described here can be extended in several directions. First, it is desirable to apply the proposed approach to a real-world social network that includes a larger number of customers. Second, the effectiveness of the proposed targeted advertising approach is influenced by the availability of a product category, and hence it is worthwhile to evaluate the effectiveness of applying the proposed approach to other types of products. Finally, the adoption of a product may propagate through a social network. Therefore, if a subset of individuals adopts a product, they may trigger a large cascade of further adoptions. Integrating the effect of influence propagation to the targeted advertising approach is therefore also an interesting direction for future work.