Towards Social Recommendation System Based on the Data from Microblogs
With the rapid growth of Internet and social networking websites, there are various services that provided in these platforms. For instance, Face book focuses on social activities, Twitter and Plurk are both focus on the interaction of users through short messages (which are so-called microblogs). Therefore, there are more than millions of users registered in these websites and become places where full of marketing possibilities. Thus, it is an important issue to assist companies to understand the users in the social networking websites in order to enhance the accuracy and efficiency of target marketing. In this paper, we have proposed the architecture of a social recommendation system based on the data from microblogs. The social recommendation system is conducted according to the messages and social structure of target users. The similarity of the discovered features of users and products will then be calculated as the essence of the recommendation engine. A case study will be included to present how the recommendation system works based on real data that collected from Plurk.