Improving Network-Based Marketing by Personalized Recommendation

Abstract

Social networks, which are a newfound phenomenon, have gained much attention. These networks, which are based on Web 2.0, provide a free and flexible environment for users and organizations to make diverse contents and, based on it, absorb users. Marketing is one of the main activities done in social networks for incoming purpose. Organizations and companies are trying to attract potential and actual customers by targeted advertising in these networks. Variety and diversity of advertising and marketing methods in social networks has made users confused and uncertain. To solve this problem, in this chapter, the authors propose a group recommender system, which is based on data mining techniques, information theory, and user preferences. This system, despite other existing methods, could yet support users who are not in relation with the others or their activity history is not available. Each group can be fans of a company or one or more products of it. The results show the superiority of this chapter’s proposed model rather than the other.