A Novel Preferential Diffusion Recommendation Algorithm Based on User’s Nearest Neighbors

Table 3

Algorithmic performance for MovieLens 100k, MovieLens 1M, and Epinions data. The precision, interuser-diversity, and novelty are corresponding to = 20. The parameters for NNMD are = 50 and = 0.9 in MovieLens 100k and MovieLens 1M, while, in Epinions, the parameters are = 10 and = 0. The entries corresponding to the best performance over all methods are emphasized in bold.

Data set	Algorithms	Ranking score	Precision	Interuser-diversity	Novelty
MovieLens 100k	NNMD	0.059537	0.2242	0.8401	237
	SMD	0.069011	0.1971	0.6970	279
MovieLens 1M	NNMD	0.077039	0.2726	0.8816	1340
	SMD	0.095269	0.1949	0.5865	1828
Epinions	NNMD	0.180439	0.0374	0.6787	204
	SMD	0.181141	0.0357	0.6743	205