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Recently big data have become a research hotspot and been successfully exploited in a few applications such as data mining and business modeling. Although big data contain a plenty of treasures for all the fields of computer science, it is very difficult for the current computing paradigms and computer hardware to efficiently process and utilize big data to attain what are looked forward to. In this work, we explore the possibility of employing big data in recommendation systems. We have proposed a simple recommendation system framework BDRSF (Big Data Recommendation System Framework), which is based on big data with social context theories and has abilities in obtaining the Recommender based on the idea of supervised learning through big data training. Its main idea can be divided into three parts: (1) reduce the scale of the current recommendation problems according to the essence of recommending; (2) design a rational Recommender and propose a novel supervised learning algorithm to get it; (3) utilize the Recommender to deal with the later recommendation problems. Experimental results show that BDRSF outperforms conventional recommendation systems, which clearly indicates the effectiveness and efficiency of big data with social context in personalized recommendation. [ABSTRACT FROM AUTHOR]