Information Technology Reference
In-Depth Information
Table 11.5
Results on the HP2003 dataset
Algorithm
NDCG@1
NDCG@3
NDCG@10
P@1
P@3
P@10
MAP
Regression
0.420
0.510
0.594
0.420
0.211
0.088
0.497
RankSVM
0.693
0.775
0.807
0.693
0.309
0.104
0.741
RankBoost
0.667
0.792
0.817
0.667
0.311
0.105
0.733
FRank
0.653
0.743
0.797
0.653
0.289
0.106
0.710
ListNet
0.720
0.813
0.837
0.720
0.320
0.106
0.766
AdaRank
0.733
0.805
0.838
0.733
0.309
0.106
0.771
SVM
map
0.713
0.779
0.799
0.713
0.309
0.100
0.742
Table 11.6
Results on the HP2004 dataset
Algorithm
NDCG@1
NDCG@3
NDCG@10
P@1
P@3
P@10
MAP
Regression
0.387
0.575
0.646
0.387
0.213
0.08
0.526
RankSVM
0.573
0.715
0.768
0.573
0.267
0.096
0.668
RankBoost
0.507
0.699
0.743
0.507
0.253
0.092
0.625
FRank
0.600
0.729
0.761
0.600
0.262
0.089
0.682
ListNet
0.600
0.721
0.784
0.600
0.271
0.098
0.690
AdaRank
0.613
0.816
0.832
0.613
0.298
0.094
0.722
SVM
map
0.627
0.754
0.806
0.627
0.280
0.096
0.718
Table 11.7
Results on the OHSUMED dataset
Algorithm
NDCG@1
NDCG@3
NDCG@10
P@1
P@3
P@10
MAP
Regression
0.446
0.443
0.411
0.597
0.577
0.466
0.422
RankSVM
0.496
0.421
0.414
0.597
0.543
0.486
0.433
RankBoost
0.463
0.456
0.430
0.558
0.561
0.497
0.441
FRank
0.530
0.481
0.443
0.643
0.593
0.501
0.444
ListNet
0.533
0.473
0.441
0.652
0.602
0.497
0.446
AdaRank
0.539
0.468
0.442
0.634
0.590
0.497
0.449
SVM
map
0.523
0.466
0.432
0.643
0.580
0.491
0.445
gorithms achieve comparable results, among which Ranking SVM seems to be
slightly better than the other two algorithms.
2. In terms of NDCG@3 and NDCG@10, ListNet and AdaRank perform much
better than the pairwise and pointwise ranking algorithms, while the performance
of SVM
map
is very similar to the pairwise ranking algorithms.
3. In terms of P@10, ListNet performs much better than the pairwise and pointwise
ranking algorithms, while the performances of AdaRank and SVM
map
are not so
good as those of the pairwise ranking algorithms.