A New Personalized Three-dimensional Recommendation Approach for C2C ECommerce Context
Ai Danxiang, Zuo Hui, Yang Jun
Journal of E-Technology, Vol. 8, No. 1, 2017.
DOI: 
Link: http://www.dline.info/jet/fulltext/v8n1/jetv8n1_3.pdf
Abstract:
Recommender systems have been viewed as powerful tools to filter overloaded information in the e-commerce
environment. But traditional two-dimensional recommendation methods, which only explore the relevance between customers
and products, are not applicable for the recommendation space in C2C (Customer to Customer) e-commerce context that
involves three types of entities: buyers, sellers and products. In this paper, we propose a three-dimensional approach to
explore the relevance among buyers, sellers and products, and provide personalized â€œseller and productâ€ recommendations
for buyers. Firstly, similarities between sellers are calculated based on seller features. Then the spare data in the threedimensional
historical rating set are supplemented and based on which buyer similarities are calculated to find neighbors
who have similar product preferences with the target buyer. Finally, a three-dimensional rating prediction model is used to
predict the unknown ratings that the buyer may give to candidate â€œseller and productâ€ combinations. A real data experiment
is conducted and the results prove the effectiveness of the proposed approach.