C2C e-commerce recommender system based on three-dimensional collaborative filtering

C2C(Consumer to Consumer) e-commerce is currently one of the main e-commerce patterns.To solve the special recommendation problem in C2C e-commerce websites,a three-dimensional collaborative filtering recommendation approach which can recommend seller and product combinations is proposed by extending the traditional two-dimensional collaborative filtering approach.And a C2C e-commerce recommender system based on the proposed approach is designed.The framework of the system and the crucial calculations in the recommendation process are discussed.The system firstly calculates seller similarities using seller features,and fills the ratings set based on sales relations and seller similarities to solve the sparsity problem of the three-dimensional rating data.Then it calculates the buyer similarities using historical ratings,decides neighbors and predicts unknown ratings by applying collaborative filtering principle.Finally it recommends the seller and product combinations with the highest prediction ratings to the target buyer.The good recommendation performance of the system is also proved by a true data experiment.