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hexiangnan / attentional_factorization_machine

TensorFlow Implementation of Attentional Factorization Machine
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Why there is little improvement of attenion model in my experiment ? #18

Open CancanZhang opened 5 years ago

CancanZhang commented 5 years ago

I tried the quick example you gave:

python FM.py --dataset ml-tag --epoch 20 --pretrain -1 --batch_size 4096 --hidden_factor 16 --lr 0.01 --keep 0.7

params: 1537566

Init: train=1.0000, validation=1.0000 [52.3 s] Epoch 1 [6.2 s] train=0.5402, validation=0.6012 [10.7 s] Epoch 2 [7.2 s] train=0.4903, validation=0.5775 [12.5 s] Epoch 3 [6.9 s] train=0.4604, validation=0.5673 [11.3 s] Epoch 4 [6.2 s] train=0.4376, validation=0.5611 [5.9 s] Epoch 5 [6.8 s] train=0.4201, validation=0.5572 [5.2 s] Epoch 6 [6.8 s] train=0.4054, validation=0.5538 [5.3 s] Epoch 7 [6.7 s] train=0.3932, validation=0.5513 [5.3 s] Epoch 8 [7.0 s] train=0.3831, validation=0.5497 [6.0 s] Epoch 9 [6.3 s] train=0.3743, validation=0.5483 [5.8 s] Epoch 10 [6.4 s] train=0.3668, validation=0.5471 [6.1 s] Epoch 11 [7.0 s] train=0.3599, validation=0.5461 [5.4 s] Epoch 12 [6.9 s] train=0.3538, validation=0.5456 [5.3 s] Epoch 13 [6.8 s] train=0.3485, validation=0.5446 [6.0 s] Epoch 14 [6.1 s] train=0.3439, validation=0.5440 [6.0 s] Epoch 15 [6.2 s] train=0.3398, validation=0.5435 [5.7 s] Epoch 16 [6.8 s] train=0.3358, validation=0.5432 [5.3 s] Epoch 17 [6.7 s] train=0.3323, validation=0.5427 [5.9 s] Epoch 18 [6.1 s] train=0.3289, validation=0.5424 [5.9 s] Epoch 19 [6.4 s] train=0.3257, validation=0.5420 [5.8 s] Epoch 20 [6.2 s] train=0.3230, validation=0.5416 [5.7 s] Save model to file as pretrain. Best Iter(validation)= 20 train = 0.3230, valid = 0.5416 [353.0 s]

python AFM.py --dataset ml-tag --epoch 20 --pretrain 1 --batch_size 4096 --hidden_factor [16,16] --keep [1.0,0.5] --lamda_attention 100.0 --lr 0.1

params: 1537870

Init: train=0.8130, validation=0.8226 [7.9 s] Epoch 1 [7.5 s] train=0.5058, validation=0.5768 [9.2 s] Epoch 2 [8.2 s] train=0.4604, validation=0.5640 [11.3 s] Epoch 3 [8.4 s] train=0.4319, validation=0.5570 [9.4 s] Epoch 4 [7.8 s] train=0.4121, validation=0.5529 [7.2 s] Epoch 5 [8.5 s] train=0.3975, validation=0.5504 [7.3 s] Epoch 6 [8.5 s] train=0.3850, validation=0.5480 [7.9 s] Epoch 7 [8.7 s] train=0.3754, validation=0.5464 [10.4 s] Epoch 8 [8.0 s] train=0.3674, validation=0.5457 [8.3 s] Epoch 9 [9.1 s] train=0.3619, validation=0.5446 [8.9 s] Epoch 10 [9.0 s] train=0.3568, validation=0.5436 [9.8 s] Epoch 11 [8.6 s] train=0.3521, validation=0.5431 [10.2 s] Epoch 12 [9.6 s] train=0.3480, validation=0.5429 [10.8 s] Epoch 13 [8.6 s] train=0.3443, validation=0.5423 [9.5 s] Epoch 14 [7.8 s] train=0.3407, validation=0.5415 [7.6 s] Epoch 15 [7.9 s] train=0.3388, validation=0.5413 [7.3 s] Epoch 16 [8.2 s] train=0.3358, validation=0.5412 [7.4 s] Epoch 17 [8.3 s] train=0.3339, validation=0.5405 [16.8 s] Epoch 18 [7.6 s] train=0.3315, validation=0.5405 [7.5 s] Epoch 19 [8.4 s] train=0.3305, validation=0.5402 [7.0 s] Epoch 20 [8.4 s] train=0.3278, validation=0.5398 [10.9 s] Best Iter(validation)= 20 train = 0.3278, valid = 0.5398 [378.1 s]

It only has 0.0018 improvements in RMSE. Then I did an experiment on Frappe dataset. I modified the parameters according to the paper in FM and used the parameters you gave in another question.

python FM.py --dataset frappe --epoch 100 --pretrain -1 --batch_size 128 --hidden_factor 256 --lr 0.01 --keep 0.8

params: 1383175

Init: train=0.9998, validation=0.9998 [42.3 s] Epoch 1 [6.3 s] train=0.3417, validation=0.4357 [3.0 s] Epoch 2 [6.1 s] train=0.2542, validation=0.3977 [1.6 s] Epoch 3 [6.0 s] train=0.2135, validation=0.3938 [1.7 s] Epoch 4 [6.1 s] train=0.1656, validation=0.3652 [1.7 s] Epoch 5 [8.0 s] train=0.1428, validation=0.3566 [1.8 s] Epoch 6 [6.5 s] train=0.1292, validation=0.3603 [1.7 s] Epoch 7 [6.4 s] train=0.1181, validation=0.3590 [1.8 s] Epoch 8 [6.5 s] train=0.1056, validation=0.3529 [1.6 s] Epoch 9 [6.4 s] train=0.0977, validation=0.3509 [1.7 s] Epoch 10 [6.5 s] train=0.0927, validation=0.3490 [1.7 s] Epoch 11 [6.3 s] train=0.0891, validation=0.3493 [1.5 s] Epoch 12 [6.3 s] train=0.0834, validation=0.3491 [1.7 s] Epoch 13 [7.6 s] train=0.0799, validation=0.3470 [1.9 s] Epoch 14 [6.3 s] train=0.0789, validation=0.3483 [1.8 s] Epoch 15 [6.5 s] train=0.0740, validation=0.3425 [1.6 s] Epoch 16 [6.0 s] train=0.0762, validation=0.3460 [1.7 s] Epoch 17 [6.0 s] train=0.0719, validation=0.3460 [1.8 s] Epoch 18 [6.0 s] train=0.0710, validation=0.3436 [1.7 s] Epoch 19 [6.1 s] train=0.0671, validation=0.3430 [1.8 s] Epoch 20 [6.3 s] train=0.0665, validation=0.3448 [1.8 s] .... Epoch 90 [6.4 s] train=0.0416, validation=0.3319 [1.6 s] Epoch 91 [6.3 s] train=0.0413, validation=0.3318 [1.7 s] Epoch 92 [6.5 s] train=0.0412, validation=0.3331 [1.7 s] Epoch 93 [6.8 s] train=0.0413, validation=0.3318 [1.8 s] Epoch 94 [6.8 s] train=0.0427, validation=0.3316 [1.6 s] Epoch 95 [7.3 s] train=0.0409, validation=0.3323 [1.9 s] Epoch 96 [7.8 s] train=0.0404, validation=0.3315 [1.8 s] Epoch 97 [8.2 s] train=0.0418, validation=0.3315 [1.6 s] Epoch 98 [6.9 s] train=0.0407, validation=0.3318 [1.8 s] Epoch 99 [6.7 s] train=0.0412, validation=0.3314 [1.7 s] Epoch 100 [8.0 s] train=0.0399, validation=0.3311 [2.2 s] Save model to file as pretrain. Best Iter(validation)= 89 train = 0.0403, valid = 0.3311 [876.8 s]

python AFM.py --keep '[1.0,0.8]' --lamda_attention 16 --hidden_factor '[256,256]' --batch_size 128 --dataset frappe --pretrain 1 --epoch 100 --valid_dimen 10 --lr 0.015

Init: train=0.7971, validation=0.8043 [7.8 s] Epoch 1 [16.7 s] train=0.3162, validation=0.4117 [6.6 s] Epoch 2 [16.2 s] train=0.2379, validation=0.3756 [6.5 s] Epoch 3 [16.9 s] train=0.1935, validation=0.3594 [7.8 s] Epoch 4 [17.4 s] train=0.1625, validation=0.3507 [6.6 s] Epoch 5 [18.5 s] train=0.1399, validation=0.3454 [9.0 s] Epoch 6 [18.7 s] train=0.1243, validation=0.3434 [6.4 s] Epoch 7 [16.2 s] train=0.1121, validation=0.3414 [6.5 s] Epoch 8 [16.4 s] train=0.1022, validation=0.3397 [6.4 s] Epoch 9 [17.4 s] train=0.0935, validation=0.3384 [7.1 s] Epoch 10 [16.3 s] train=0.0874, validation=0.3365 [7.8 s] Epoch 11 [19.0 s] train=0.0806, validation=0.3365 [6.8 s] Epoch 12 [16.2 s] train=0.0761, validation=0.3362 [6.3 s] Epoch 13 [16.1 s] train=0.0721, validation=0.3352 [6.4 s] Epoch 14 [16.1 s] train=0.0690, validation=0.3356 [6.3 s] Epoch 15 [16.1 s] train=0.0652, validation=0.3353 [6.3 s] Epoch 16 [16.2 s] train=0.0629, validation=0.3351 [6.4 s] Epoch 17 [16.2 s] train=0.0613, validation=0.3340 [6.3 s] Epoch 18 [16.2 s] train=0.0587, validation=0.3347 [6.4 s] Epoch 19 [16.2 s] train=0.0578, validation=0.3338 [6.3 s] Epoch 20 [16.2 s] train=0.0561, validation=0.3331 [6.6 s] Epoch 21 [17.0 s] train=0.0540, validation=0.3344 [7.8 s] Epoch 22 [17.5 s] train=0.0524, validation=0.3333 [8.8 s] Epoch 23 [17.6 s] train=0.0514, validation=0.3336 [6.6 s] Epoch 24 [16.2 s] train=0.0501, validation=0.3331 [6.4 s] Epoch 25 [16.2 s] train=0.0501, validation=0.3339 [6.6 s] Epoch 26 [16.2 s] train=0.0493, validation=0.3328 [6.5 s] Epoch 27 [16.8 s] train=0.0483, validation=0.3337 [7.0 s] Epoch 28 [18.0 s] train=0.0475, validation=0.3327 [6.4 s] Epoch 29 [16.5 s] train=0.0471, validation=0.3331 [9.7 s] Epoch 30 [19.3 s] train=0.0466, validation=0.3328 [6.3 s] Epoch 31 [16.2 s] train=0.0461, validation=0.3326 [6.6 s] Epoch 32 [16.2 s] train=0.0452, validation=0.3328 [6.5 s] Epoch 33 [16.2 s] train=0.0447, validation=0.3325 [6.2 s] Epoch 34 [16.2 s] train=0.0447, validation=0.3329 [6.3 s] Epoch 35 [16.2 s] train=0.0437, validation=0.3324 [6.5 s] Epoch 36 [16.2 s] train=0.0436, validation=0.3322 [6.4 s] Epoch 37 [16.4 s] train=0.0429, validation=0.3317 [6.3 s] Epoch 38 [16.2 s] train=0.0420, validation=0.3321 [7.2 s] Epoch 39 [17.4 s] train=0.0425, validation=0.3317 [6.3 s] Epoch 40 [18.8 s] train=0.0418, validation=0.3315 [8.3 s] Epoch 41 [18.9 s] train=0.0411, validation=0.3317 [7.0 s] Epoch 42 [16.3 s] train=0.0412, validation=0.3316 [6.5 s] Epoch 43 [16.2 s] train=0.0404, validation=0.3317 [6.5 s] Epoch 44 [16.3 s] train=0.0409, validation=0.3319 [6.6 s] Epoch 45 [16.4 s] train=0.0408, validation=0.3316 [6.6 s] Epoch 46 [16.4 s] train=0.0396, validation=0.3313 [6.4 s] Epoch 47 [16.2 s] train=0.0395, validation=0.3313 [6.3 s] Epoch 48 [16.1 s] train=0.0390, validation=0.3316 [6.2 s] Epoch 49 [16.1 s] train=0.0387, validation=0.3310 [6.3 s] Epoch 50 [16.1 s] train=0.0399, validation=0.3313 [6.4 s] Epoch 51 [16.1 s] train=0.0387, validation=0.3312 [6.2 s] Epoch 52 [16.0 s] train=0.0390, validation=0.3317 [6.2 s] Epoch 53 [16.0 s] train=0.0387, validation=0.3317 [6.2 s] Epoch 54 [16.3 s] train=0.0384, validation=0.3310 [6.2 s] Epoch 55 [16.0 s] train=0.0379, validation=0.3311 [6.5 s] Epoch 56 [16.3 s] train=0.0384, validation=0.3310 [6.3 s] Epoch 57 [16.0 s] train=0.0383, validation=0.3311 [6.3 s] Epoch 58 [16.0 s] train=0.0373, validation=0.3311 [6.5 s] Epoch 59 [16.1 s] train=0.0374, validation=0.3306 [6.2 s] Epoch 60 [16.1 s] train=0.0371, validation=0.3308 [6.3 s] Epoch 61 [16.1 s] train=0.0372, validation=0.3313 [6.4 s] Epoch 62 [16.1 s] train=0.0366, validation=0.3305 [6.3 s] Epoch 63 [16.2 s] train=0.0368, validation=0.3308 [6.3 s] Epoch 64 [16.3 s] train=0.0365, validation=0.3307 [6.6 s] Epoch 65 [18.0 s] train=0.0365, validation=0.3304 [6.7 s] Epoch 66 [19.3 s] train=0.0362, validation=0.3309 [8.2 s] Epoch 67 [16.5 s] train=0.0359, validation=0.3302 [6.2 s] Epoch 68 [16.1 s] train=0.0360, validation=0.3303 [6.4 s] Epoch 69 [16.0 s] train=0.0359, validation=0.3306 [6.2 s] Epoch 70 [16.2 s] train=0.0360, validation=0.3303 [6.2 s] Epoch 71 [16.4 s] train=0.0358, validation=0.3304 [6.2 s] Epoch 72 [16.3 s] train=0.0359, validation=0.3305 [6.4 s] Epoch 73 [16.3 s] train=0.0359, validation=0.3299 [6.1 s] Epoch 74 [16.2 s] train=0.0354, validation=0.3303 [6.3 s] Epoch 75 [16.2 s] train=0.0356, validation=0.3303 [6.6 s] Epoch 76 [16.5 s] train=0.0357, validation=0.3305 [6.6 s] Epoch 77 [16.2 s] train=0.0351, validation=0.3302 [6.4 s] Epoch 78 [16.2 s] train=0.0351, validation=0.3296 [6.2 s] Epoch 79 [16.1 s] train=0.0345, validation=0.3301 [6.2 s] Epoch 80 [16.1 s] train=0.0342, validation=0.3297 [6.5 s] Epoch 81 [16.3 s] train=0.0354, validation=0.3299 [6.3 s] Epoch 82 [16.1 s] train=0.0349, validation=0.3300 [6.2 s] Epoch 83 [16.1 s] train=0.0344, validation=0.3298 [6.5 s] Epoch 84 [16.2 s] train=0.0342, validation=0.3296 [6.3 s] Epoch 85 [16.1 s] train=0.0341, validation=0.3295 [6.3 s] Epoch 86 [16.0 s] train=0.0341, validation=0.3297 [6.3 s] Epoch 87 [16.3 s] train=0.0343, validation=0.3299 [6.5 s] Epoch 88 [16.2 s] train=0.0338, validation=0.3297 [6.3 s] Epoch 89 [16.1 s] train=0.0335, validation=0.3295 [6.1 s] Epoch 90 [16.0 s] train=0.0339, validation=0.3294 [6.4 s] Epoch 91 [16.4 s] train=0.0339, validation=0.3296 [6.4 s] Epoch 92 [16.1 s] train=0.0332, validation=0.3294 [6.2 s] Epoch 93 [16.1 s] train=0.0333, validation=0.3295 [6.5 s] Epoch 94 [16.3 s] train=0.0329, validation=0.3293 [6.3 s] Epoch 95 [16.0 s] train=0.0335, validation=0.3292 [6.2 s] Epoch 96 [16.9 s] train=0.0335, validation=0.3295 [6.4 s] Epoch 97 [17.6 s] train=0.0330, validation=0.3297 [6.2 s] Epoch 98 [16.0 s] train=0.0330, validation=0.3295 [6.3 s] Epoch 99 [16.0 s] train=0.0335, validation=0.3296 [6.4 s] Epoch 100 [16.2 s] train=0.0334, validation=0.3293 [7.4 s] Best Iter(validation)= 95 train = 0.0335, valid = 0.3292 [2333.9 s]

In this experiment, it still only has 0.0019 improvement in RMSE.

In fact, I also tried an experiment adding regularization (lambda 0.1) in FM model to avoided over-fitting and got 0.1318 RMSE in training data and 0.3468 RMSE in test data. Although AFM got 0.3372 RMSE in test data (0.0096 improvements), it is still far away from our expectation.

I am wondering why the result of AFM relies on a good pre-trained FM model and what kind of FM model is suitable to get such improvement?

Thanks.

CancanZhang commented 5 years ago

Could u please share your pre-trained FM model?