A personalized recommendation algorithm via heterogeneous heat conduction

Heat conduction analogous process has ever been introduced into information filtering named standard heat conduction (SHC) method, resulting in a highly personalized but less accurate recommendation. In order to improve the recommendation accuracy, different algorithms have been proposed, with typical examples to be the highly accurate mass diffusion (MD) method, and a both highly accurate and highly diverse biased heat-conduction method (BHC). These previous algorithms have not considered the rating effect, where ratings essentially depict how users like objects. In this article, we propose a heterogeneous heat conduction method (HHC), by taking the ratings as the weight of heat conduction, which thus generates a heterogeneous heat diffusion pattern. Experimental results obtained from the Movie Lens dataset show that, the HHC greatly enhances the recommendation accuracy against the SHC, with the improvement percentage to be 46.32%, and also elevates the recommendation accuracy against the MD as well as the BHC. Moreover, the HHC simultaneously outperforms the MD, and even the BHC in recommendation diversity.