Users’ Rating Predictions Using Collaborating Filtering Based on Users and Items Similarity Measures

Table 7

Comparison of BL methods with Item-Item (Pearson Correlation) method.

Algorithms	RMSE			MAE
	ML	ML	Ciao DVD	ML 100k	ML	Ciao DVD
	100k	1M			1M
Slope one	0.951	0.902	1.093	0.749	0.711	0.816
Random	2.105	2.138	2.307	1.723	1.762	1.888
Global average	1.131	1.115	1.079	0.950	0.933	0.833
User item baseline	0.952	0.909	0.976	0.755	0.720	0.762
Item K-NN (Pearson correlation)	0.933	0.879	0.964	0.734	0.690	0.734