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Table 6 Evaluation results

From: Multi-criteria collaborative filtering recommender by fusing deep neural network and matrix factorization

Dataset Metric SVD SVD++ SlopeOne Baseline estimates Single DNN DMCCF [19] Our
TripAdvisor MAE 0.8071 ± 0.0043 0.8222 ± 0.0071 0.9160 ± 0.0053 0.8094 ± 0.0019 0.7855 ± 0.0153 0.7552 ± 0.0050 0.7434 ± 0.0068
F1 0.8370 ± 0.0035 0.8390 ± 0.0035 0.8666 ± 0.0018 0.8391 ± 0.0020 0.8572 ± 0.0024 0.8769 ± 0.0043 0.8811 ± 0.0031
F2 0.7951 ± 0.0031 0.7991 ± 0.0057 0.9248 ± 0.0011 0.7986 ± 0.0029 0.9176 ± 0.0057 0.9295 ± 0.0014 0.9319 ± 0.0204
FCP 0.5024 ± 0.0210 0.5092 ± 0.0056 0.5096 ± 0.0661 0.5025 ± 0.0117 0.4957 ± 0.0102 0.5126 ± 0.0160 0.5164 ± 0.0048
MAP 0.3935 ± 0.0057 0.3483 ± 0.0049 0.3006 ± 0.0026 0.3881 ± 0.0023 0.4481 ± 0.0091 0.4643 ± 0.0072 0.4836 ± 0.0047
MRR 0.7272 ± 0.0054 0.7513 ± 0.0018 0.7502 ± 0.0037 0.7374 ± 0.0028 0.7244 ± 0.0092 0.7664 ± 0.0039 0.7693 ± 0.0036
Movies dataset MAE 2.1072 ± 0.0168 2.1594 ± 0.0050 2.1798 ± 0.0112 2.1333 ± 0.0069 2.0613 ± 0.0347 2.0474 ± 0.0119 2.0188 ± 0.0181
F1 0.8061 ± 0.0049 0.7985 ± 0.0010 0.7860 ± 0.0026 0.8107 ± 0.0031 0.8344 ± 0.0106 0.8414 ± 0.0041 0.8563 ± 0.0036
F2 0.7608 ± 0.0062 0.7553 ± 0.0023 0.7325 ± 0.0045 0.7699 ± 0.0045 0.8256 ± 0.0287 0.8383 ± 0.0049 0.8442 ± 0.0056
FCP 0.6022 ± 0.0107 0.5985 ± 0.0029 0.6251 ± 0.0098 0.6233 ± 0.0027 0.6226 ± 0.0055 0.6258 ± 0.0083 0.6289 ± 0.0029
MAP 0.1910 ± 0.0056 0.2144 ± 0.0035 0.2089 ± 0.0067 0.1612 ± 0.0027 0.2136 ± 0.0143 0.2345 ± 0.0109 0.2458 ± 0.0090
MRR 0.6651 ± 0.0091 0.6502 ± 0.0048 0.6085 ± 0.0048 0.6721 ± 0.0052 0.6922 ± 0.0145 0.7061 ± 0.0078 0.7092 ± 0.0049
  1. Evaluation results for our model against the other algorithms. F1, F2, FCP, MAP, and MRR the higher the better while MAE the lower the better