A Personalized Hybrid Recommendation System Oriented to E-Commerce Mass Data in the Cloud
Fang Dong, Junzhou Luo, Xia Zhu, Yuxiang Wang, Jun Shen · 2013
Personalized recommendation technology in E-commerce is widespread to solve the problem of product information overload. However, with the further growth of the number of E-commerce users and products, the original recommendation algorithms and systems will face several new challenges: (1) to model user's interests more accurately, (2) to provide more diverse recommendation modes, and (3) to support large-scale expansion. To address these challenges, from the actual demands of E-commerce applications (as Made-in-China website), a personalized hybrid recommendation system, which can support massive data set, is designed and implemented in this paper by using Cloud technology. Hereinto, the recommendation algorithms are designed based on a novel user interesting model for different scenarios, and the massive data parallel processing techniques in Cloud computing is utilized to realize the effective execution of recommendation algorithms. Finally, several experiments are presented to highlight the system performance.