Personalized recommendation technology in Ecommerce 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.
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