SOCIAL NETWORK ANALYSIS OF C2C E-COMMERCE RECOMMENDER SYSTEM BASED ON LINK PREDICTION BY EMPLOYING GRAPH BASED APPROACH
Social network analysis emerged as an important research topic in sociology decades ago, and it has also attracted scientists from various fields of study like psychology, anthropology, geography and economics. This paper focuses on building a recommendation algorithm for C2C (Consumer-to-Consumer) e-commerce business model by considering special features of C2C e-commerce websites. In this paper, users and their transactions considered as a network based on link prediction using a graph based technique. This is an important task in social network analysis is used to build the recommender system. The proposed tow-level recommendation algorithm, rather than topology of the network, uses nodes’ features like category of items, ratings of users, and reputation of sellers. The links predicted can be presented as recommendations to the user. This paper also focuses on how graph algorithm can be used to improve recommendation in ecommerce websites. The method incorporates semantic recommendation using overlap technique based in graph. The results show that the proposed model can be used to predict a portion of future trades between users in a C2C commercial network.
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