Initial configuration effect on personalized recommendation in a biased heat-conduction algorithm

Initial configuration effect is investigated for a both highly accurate and highly diverse biased heat conduction method. According to the individual object degree, we assign a heterogeneous initial resource for each object. Experimental results obtained from the MovieLens dataset show that, the proposed method outperforms the standard heat conduction method by 47.33%, and also outperforms an accurate mass diffusion method by 24.04% in recommendation accuracy. Especially, even compared with an excellent hybrid method of heat conduction and mass diffusion, and the original biased heat conduction method, the manifested method further enhances both the recommendation accuracy and the diversity.