Recommendation algorithm is very important for e-commercial websites when it can recommend online customers favorite products, which results out an increase in sale revenue. The project produces the framework of e-commercial recommendation algorithms, named Hudup. This is a middleware framework or “operating system” for e-commercial recommendation software, which support scientists and software developers build up their own recommendation algorithms with low cost, high achievement and fast speed. Concretely, you need to develop a recommendation solution for online-sale website. You, a scientist, invent a new algorithm after researching many years. Your solution is excellent and very useful but you cope with many difficulties relevant to heterogeneous models in recommendation studies, processing data, evaluation metrics, and simulation environment. Hudup supports you to solve perfectly these difficulties via three stages such as base stage – building up your algorithm, evaluation stage – evaluating your algorithm according to pre-defined metrics, and simulation stage – providing a simulator which helps you to test your algorithm in real-time applications. The product introduction is accepted to be published in American Journal of Computer Science and Information Engineering, American Association for Science and Technology (AASCIT). The product is available at
http://www.hudup.net.