چكيده لاتين :
There has been a big revolution in electronic commerce since the
advent of recommender systems. Most of the current recommender systems are
designed for B2C e-commerce sites. But this paper focuses on building a
recommendation algorithm that increases volume and speed of forming trades
between users by considering special features of C2C e-commerce sites. In this
paper, we consider users and transactions between them as a network in which
nodes represent users and edges represent transactions between them. By this
mapping, link prediction approaches could be used to build the recommender
system. The proposed model, rather than topology of the network, uses nodes’
features like: category of items, ratings of users, and reputation of sellers. The
results show that the proposed model can be used to predict future trades
between users in a C2C commercial network.
