Implement of item-based recommendation on GPU

Recommemder System is becoming more and more important for getting information in recent 20 years. But recommender system has the weakness of extreed large scale that makes it delayable for recommendation, which making it cannot offer real-time service. Business recommender system is general divided into two parts, the on-line recommend part and the off-line calculation part. It precomputes the off-line part to get quicker recommendation when needed. Pretended real-time recommendation is a compromise with the growing and changing system. We propose the better way to get better real-time service by processing the off-line calculation on GPU, which is a high-speed parallel processor, to speed up the first part of recommender system to get more real-time service. Our experiments show, the off-line part can speed up 19 times when using GPU, and the larger of the data scale, the better it can improve.