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Social network analysis emerged as an important research topic in sociologydecades ago, and it has also attracted scientists from various fields of studylike psychology, anthropology, geography and economics. In recent years, asignificant number of researches has been conducted on using social networkanalysis to design e-commerce recommender systems. Most of the currentrecommender systems are designed for B2C e-commerce websites. This paperfocuses on building a recommendation algorithm for C2C e-commerce businessmodel by considering special features of C2C e-commerce websites. In thispaper, we consider users and their transactions as a network; by this mapping,link prediction technique which is an important task in social network analysiscould be used to build the recommender system. The proposed tow-levelrecommendation algorithm, rather than topology of the network, uses nodesfeatures like: category of items, ratings of users, and reputation of sellers.The results show that the proposed model can be used to predict a portion offuture trades between users in a C2C commercial network.

A Novel C2C E-Commerce Recommender System Based on Link Prediction: Applying Social Network Analysis