An FPGA-Based Accelerator for Neighborhood-Based Collaborative Filtering Recommendation Algorithms

Neighborhood-based Collaborative Filtering (CF) is a kind of techniques in the field of recommendation algorithms and has been widely used in lots of personalized recommender systems. In the big data era, the increasing data amounts make these CF recommendation algorithms become time-consuming and energy-wasted. At present, Cloud computing and Graphic Processing Unit (GPU) are the two major platforms to accelerate CF algorithms. However, both platforms exist some remarkable shortcomings such as efficiency and power. To solve these problems, in our work, we investigate three neighborhood-based CF algorithms and design a general and flexible accelerator for them based on Field Programmable Gate Array (FPGA). This accelerator cooperates with host CPU and could accelerates primary time-consuming parts that these algorithms share. Experimental results show that our accelerator could significantly improve the acceleration efficiency with the affordable hardware cost and less energy consumption.