A New Collaborative Recommendation Approach Based on Users Clustering Using Artificial Bee Colony Algorithm

Table 3

Algorithmic performance for the real-world dataset. The ranking score, precision, recall, intrasimilarity, Hamming distance are corresponding to 𝐿 = 50, 60, and 70. Each number presented in this table is obtained by averaging over five runs, each of which has an independently random division of training set and test.

Algorithms	Ranking score	Precision	Recall	Intrasimilarity	Hamming distance
𝐿=50
CF	0.052	0.035	0.171	0.364	0.479
MCF	0.043	0.042	0.182	0.328	0.527
NN-; CosNgbr	0.042	0.041	0.184	0.332	0.523
cluster-based CF	0.034	0.047	0.201	0.272	0.614
𝐿=60
CF	0.046	0.032	0.185	0.385	0.461
MCF	0.040	0.039	0.194	0.342	0.506
NN-; CosNgbr	0.039	0.038	0.196	0.348	0.504
cluster-based CF	0.031	0.043	0.209	0.281	0.603
𝐿=70
CF	0.041	0.030	0.202	0.403	0.457
MCF	0.035	0.036	0.216	0.362	0.502
NN-; CosNgbr	0.034	0.035	0.217	0.364	0.499
cluster-based CF	0.027	0.040	0.231	0.293	0.592