چكيده به لاتين
Abstract:
Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. Purchasing decisions are often strongly influenced by people who the consumer knows and trusts. Moreover, many online shoppers tend to wait for the opinions of early adopters before making a purchase decision to reduce the risk of buying a new product. Web-based social communities, actively fostered by E-commerce companies, allow consumers to share their personal experiences by writing reviews, rating others’ reviews, and chatting among trusting members.
In this paper, we present an overview of the impact of social influence (Facebook) in E-commerce decision making to provide guidance to researchers and companies who have an interest in related issues. The first step is to identify influential people using their social standing and social behavior. By using topics of graph theory, we define an algorithm which can find the user’s mutual behavior. Then for the nodes that was selected by our algorithm we run infectious diseases model and then we obtain the impact of those nodes on the network. In the second step we design a recommendation system which uses user’s behavior and user profiles, to propose the nearest offer to them. To find similar mechanism to investigate and identify the users, we use data mining techniques. Finally, we evaluate the performance of the defined algorithms on real data and the results indicates that the method presented in this study is Efficient.
Keywords: Social Networks, Marketing, Graph Theory, Recommendation System
