Chapter 8 Conclusion and Future Work 125

C.2 Table Results for MovieLens

k= 1 k= 3 k= 5

FB 0.46013 0.5329 0.5797

LM 0.5192 0.5231 0.5206

δF B 41/23 p=0.0335(+) 31/33 p=0.9005(=) 24/40 p=0.0607(=)

LMW 0.5294 0.5190 0.5168

δF B 43/21 p=0.0086(+) 32/32 p=1(=) 22/42 p=0.0175(-)

δLM 16/15 p=1(=) 20/19 p=1(=) 22/17 p=0.5218(=)

LM-MF 0.5554 0.6156 0.5606

δF B 42/22 p=0.0175(+) 42/22 p=0.0175(+) 29/34 p=0.6142(=) δLM 38/19 p=0.0171(+) 42/19 p=0.0048(+) 40/21 p=0.0211(+)

LM-MF-Reg 0.5936 0.5801 0.5855

δF B 46/18 p=0.0007(+) 39/25 p=0.1041(=) 31/32 p=1(=) δLM 42/21 p=0.0117(+) 40/21 p=0.0211(+) 45/17 p=0.0006(+) Table C.3: NDCG@k results on meta-mining for the Full Cold Start setting. For each method, we give the comparison results against the full memory-base and LambdaMART methods in the rows denoted byδF B andδLM respectively. The table explanation is as in table C.2.

δU B 247/223 p=0.2887(=) 233/238 p=0.8537(=) 1392/1600 p=0.0001(-) 1545/1467 p=0.1606(=)

LMW 0.6252 0.6241 0.6455 0.6450

δU B 254/216 p=0.0878(=) 243/228 p=0.5188(=) 1559/1421 p=0.0120(+) 1546/1465 p=0.1448(=) δLM 160/153 p=0.7345(=) 136/133 p=0.9029(=) 1750/1021 p=0.0000(+) 690/715 p=0.5219(=)

LM-MF 0.6439 0.6455 0.6694 0.6700

δU B 267/203 p=0.0036(+) 253/218 p=0.1171(=) 1673/1303 p=0.0000(+) 1756/1253 p=0.0000(+) δLM 267/168 p=2e−06(+) 265/170 p=6e−06(+) 1935/950 p=0.0000(+) 1622/1140 p=0.0000(+)

LM-MF-Reg 0.6503 0.6581 0.6694 0.6705

δU B 279/190 p=4e−05(+) 276/194 p=0.0001(+) 1715/1257 p=0.0000(+) 1725/1283 p=0.0000(+) δLM 270/171 p=3e−06(+) 278/159 p=0.0000(+) 1982/862 p=0.0000(+) 1606/1081 p=0.0000(+)

Table C.4: NDCG@k results on the two MovieLens datasets for the User Cold Start setting. For each method, we give the comparison results against the user memory-based and LambdaMART methods in the rows denoted by δU B and δLM respectively. More precisely we report the numbers of wins/losses, thep-values of the McNemar’s test on these values, and denote by (+) a statistically significant improvement, by (=) no performance difference and by (-) a significant loss. In bold, the best method for a givenk.

100K 1M

k= 5 k= 10 k= 5 k= 10

FB 0.5452 0.5723 0.5339 0.5262

LM 0.5486 0.5641 0.5588 0.5597

δF B 238/231 p=0.7817(=) 244/227 p=0.4609(=) 1632/1367 p=1e−06(+) 1614/1403 p=0.0001(+)

LMW 0.5549 0.5622 0.55737 0.5631

δF B 244/225 p=0.4058(=) 221/249 p=0.2129(=) 1620/1377 p=9e−06(+) 1640/1376 p=1e−06(+) δLM 91/58 p=0.0087(+) 91/94 p=0.8830(=) 1105/1170 p=0.1796(=) 655/496 p=3e−06(+)

LM-MF 0.5893 0.5876 0.5733 0.5750

δF B 266/204 p=0.0048(+) 247/223 p=0.2887(=) 1720/1279 p=0.0000(+) 1711/1305 p=0.0000(+) δLM 272/173 p=3e−06(+) 271/188 p=0.0001(+) 1587/1219 p=0.0000(+) 1573/1187 p=0.0000(+)

LM-MF-Reg 0.5699 0.57865 0.5736 0.5683

δF B 261/207 p=0.0142(+) 250/220 p=0.1810(=) 1714/1288 p=0.0000(+) 1647/1368 p=4e−07(+) δLM 251/204 p=0.0310(+) 243/204 p=0.0722(=) 1596/1219 p=0.0000(+) 1520/1281 p=0.0000(+)

Table C.5: NDCG@k results on the two MovieLens datasets for the Full Cold Start set-ting. For each method, we give the comparison results against the full memory-base and LambdaMART methods in the rows denoted by δF B and δLM respectively. The table annotation is as before.

C.2. Table Results for MovieLens

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Dans le document Meta-mining: a meta-learning framework to support the recommendation, planning and optimization of data mining workflows (Page 162-176)