support to Koren's neighborhood model and integrated model

[mmanzato]

This pull request is related only to Koren's models. So please, consider only the commit 879654b and new ones (if exist).

[zenogantner]

Hi, what kind of results did you get with KorenImplicitKNN? I get an RMSE of 1.13 for

bin/rating_prediction --training-file=u.data --test-ratio=0.5 --recommender=KorenImplicitKNN --recommender-options="num_factors=5" --data-dir=data/ml-100k --find-iter=1

which is not particularly good.

IntegratedSVDPlusPlusKNN seems to converge nicely.

Did you calibrate the methods against what was reported in the paper?

[mmanzato]

Hello Zeno,

Currently I am on vacation, but I will check as soon as I come back to work.

Regards!

Em sexta-feira, 25 de janeiro de 2013, Zeno Gantner escreveu:

Hi, what kind of results did you get with KorenImplicitKNN? I get an RMSE
of 1.13 for

bin/rating_prediction --training-file=u.data --test-ratio=0.5
--recommender=KorenImplicitKNN --recommender-options="num_factors=5"
--data-dir=data/ml-100k --find-iter=1

which is not particularly good.

IntegratedSVDPlusPlusKNN seems to converge nicely.

Did you calibrate the methods against what was reported in the paper?

—
Reply to this email directly or view it on GitHubhttps://github.com//pull/376#issuecomment-12718590.

---

Prof. Marcelo G. Manzato
Computer Science Department (SCC)
Mathematics and Computer Science Institute (ICMC)
University of Sao Paulo (USP)
Sao Carlos-SP Brazil

+55 16 3373 6638

[mmanzato]

Hello Zeno!

Sorry for the delay, I was busy with other things here at the University.

I checked the code, and realize a mistake within the KorenImplicitKNN
class. Actually a single line was missing at the beginning of the
InitNeighborhoodMode() method:

current_learnrate = LearnRate;

The current_learnrate variable was initialized with 0, and because of that,
all parameters updates were useless.

Now I run again and it seems to be converging (alhough I still can't figure
out best parameters values for it):

rating_prediction --measures=RMSE,MAE
--training-file=/home/manzato/Databases/ml-100k/u.data
--recommender=KorenImplicitKNN --test-ratio=0.2 --rating-type=byte
--recommender-options="num_factors=5 learn_rate=0.01 bias_reg=0.01 reg=0.5
num_iter=40 frequency_regularization=true K=30 decay=0.9" --find-iter=1
loading_time 0.2
memory 1
ratings range: [1, 5]
test ratio 0.2
training data: 943 users, 1659 items, 80000 ratings, sparsity 94.88634
test data: 941 users, 1412 items, 20000 ratings, sparsity 98.49476
KorenImplicitKNN K=30 regularization=0.5 bias_reg=0.01
frequency_regularization=True learn_rate=0.01 bias_learn_rate=0.7
num_iter=0 decay=0.9
RMSE 1.123809 MAE 0.940299 new items: RMSE 1.79243 MAE 1.57211 CBD 0.36482
iteration 0
RMSE 0.9861695 MAE 0.7927129 new items: RMSE 1.14565 MAE 0.92523 CBD
0.25277 iteration 1
RMSE 0.9641793 MAE 0.7687995 new items: RMSE 1.09936 MAE 0.88151 CBD
0.24551 iteration 2
RMSE 0.9552662 MAE 0.7598233 new items: RMSE 1.08562 MAE 0.87425 CBD
0.24386 iteration 3
RMSE 0.950482 MAE 0.7551695 new items: RMSE 1.08032 MAE 0.87287 CBD 0.24332
iteration 4
RMSE 0.9475589 MAE 0.7523091 new items: RMSE 1.0783 MAE 0.87301 CBD 0.24316
iteration 5
RMSE 0.9456433 MAE 0.7504052 new items: RMSE 1.07776 MAE 0.87364 CBD
0.24314 iteration 6
RMSE 0.9443214 MAE 0.7490677 new items: RMSE 1.07789 MAE 0.87443 CBD 0.2432
iteration 7
RMSE 0.9433836 MAE 0.7481087 new items: RMSE 1.07834 MAE 0.87522 CBD
0.24328 iteration 8
RMSE 0.9427018 MAE 0.7474058 new items: RMSE 1.07892 MAE 0.87597 CBD
0.24336 iteration 9
RMSE 0.942196 MAE 0.7468699 new items: RMSE 1.07954 MAE 0.87665 CBD 0.24345
iteration 10
RMSE 0.9418148 MAE 0.7464592 new items: RMSE 1.08015 MAE 0.87725 CBD
0.24353 iteration 11
RMSE 0.9415229 MAE 0.7461361 new items: RMSE 1.08072 MAE 0.87779 CBD 0.2436
iteration 12
RMSE 0.9412969 MAE 0.7458754 new items: RMSE 1.08126 MAE 0.87827 CBD
0.24367 iteration 13
RMSE 0.9411201 MAE 0.7456625 new items: RMSE 1.08175 MAE 0.87869 CBD
0.24373 iteration 14
RMSE 0.9409808 MAE 0.7454898 new items: RMSE 1.08219 MAE 0.87906 CBD
0.24379 iteration 15
RMSE 0.94087 MAE 0.7453492 new items: RMSE 1.08259 MAE 0.87939 CBD 0.24384
iteration 16
RMSE 0.9407812 MAE 0.7452323 new items: RMSE 1.08295 MAE 0.87968 CBD
0.24388 iteration 17
RMSE 0.9407096 MAE 0.7451338 new items: RMSE 1.08328 MAE 0.87994 CBD
0.24392 iteration 18
RMSE 0.9406515 MAE 0.745051 new items: RMSE 1.08357 MAE 0.88016 CBD 0.24395
iteration 19
RMSE 0.9406041 MAE 0.7449796 new items: RMSE 1.08383 MAE 0.88037 CBD
0.24398 iteration 20
RMSE 0.9405651 MAE 0.7449183 new items: RMSE 1.08406 MAE 0.88054 CBD
0.24401 iteration 21
RMSE 0.9405329 MAE 0.7448654 new items: RMSE 1.08427 MAE 0.8807 CBD 0.24404
iteration 22
RMSE 0.9405063 MAE 0.7448199 new items: RMSE 1.08446 MAE 0.88084 CBD
0.24406 iteration 23
RMSE 0.940484 MAE 0.7447811 new items: RMSE 1.08463 MAE 0.88097 CBD 0.24408
iteration 24
RMSE 0.9404655 MAE 0.7447473 new items: RMSE 1.08478 MAE 0.88108 CBD
0.24409 iteration 25
RMSE 0.9404498 MAE 0.7447181 new items: RMSE 1.08492 MAE 0.88118 CBD
0.24411 iteration 26
RMSE 0.9404366 MAE 0.7446926 new items: RMSE 1.08504 MAE 0.88127 CBD
0.24412 iteration 27
RMSE 0.9404254 MAE 0.7446704 new items: RMSE 1.08515 MAE 0.88135 CBD
0.24414 iteration 28
RMSE 0.9404159 MAE 0.744651 new items: RMSE 1.08525 MAE 0.88142 CBD 0.24415
iteration 29
RMSE 0.9404078 MAE 0.7446339 new items: RMSE 1.08533 MAE 0.88149 CBD
0.24416 iteration 30
RMSE 0.9404008 MAE 0.7446187 new items: RMSE 1.08541 MAE 0.88154 CBD
0.24417 iteration 31
RMSE 0.9403948 MAE 0.7446055 new items: RMSE 1.08548 MAE 0.88159 CBD
0.24417 iteration 32
RMSE 0.9403896 MAE 0.744594 new items: RMSE 1.08555 MAE 0.88164 CBD 0.24418
iteration 33
RMSE 0.9403852 MAE 0.7445838 new items: RMSE 1.08561 MAE 0.88168 CBD
0.24419 iteration 34
RMSE 0.9403813 MAE 0.7445748 new items: RMSE 1.08566 MAE 0.88172 CBD
0.24419 iteration 35
RMSE 0.940378 MAE 0.744567 new items: RMSE 1.0857 MAE 0.88175 CBD 0.2442
iteration 36
RMSE 0.940375 MAE 0.7445599 new items: RMSE 1.08574 MAE 0.88178 CBD 0.2442
iteration 37
RMSE 0.9403724 MAE 0.7445537 new items: RMSE 1.08578 MAE 0.88181 CBD
0.24421 iteration 38
RMSE 0.9403701 MAE 0.7445481 new items: RMSE 1.08582 MAE 0.88183 CBD
0.24421 iteration 39
RMSE 0.9403682 MAE 0.7445431 new items: RMSE 1.08585 MAE 0.88185 CBD
0.24422 iteration 40
RMSE 0.9403664 MAE 0.7445387 new items: RMSE 1.08587 MAE 0.88187 CBD
0.24422 iteration 41
RMSE 0.9403649 MAE 0.7445347 new items: RMSE 1.0859 MAE 0.88189 CBD 0.24422
iteration 42
RMSE 0.9403635 MAE 0.7445311 new items: RMSE 1.08592 MAE 0.88191 CBD
0.24422 iteration 43
RMSE 0.9403623 MAE 0.7445278 new items: RMSE 1.08594 MAE 0.88192 CBD
0.24423 iteration 44
RMSE 0.9403611 MAE 0.7445249 new items: RMSE 1.08596 MAE 0.88193 CBD
0.24423 iteration 45
RMSE 0.9403601 MAE 0.7445223 new items: RMSE 1.08597 MAE 0.88195 CBD
0.24423 iteration 46
RMSE 0.9403592 MAE 0.74452 new items: RMSE 1.08599 MAE 0.88196 CBD 0.24423
iteration 47
RMSE 0.9403585 MAE 0.744518 new items: RMSE 1.086 MAE 0.88196 CBD 0.24423
iteration 48
RMSE 0.9403577 MAE 0.7445161 new items: RMSE 1.08601 MAE 0.88197 CBD
0.24423 iteration 49
RMSE 0.9403571 MAE 0.7445144 new items: RMSE 1.08602 MAE 0.88198 CBD
0.24424 iteration 50

Would you like me to change it in the Pull Request so that you can analyze
a possible merge?

Thank you,
Marcelo

On Sat, Jan 26, 2013 at 12:21 PM, Marcelo Manzato mmanzato@icmc.usp.brwrote:

Hello Zeno,

Currently I am on vacation, but I will check as soon as I come back to
work.

Regards!

Em sexta-feira, 25 de janeiro de 2013, Zeno Gantner escreveu:

Hi, what kind of results did you get with KorenImplicitKNN? I get an RMSE

of 1.13 for

bin/rating_prediction --training-file=u.data --test-ratio=0.5
--recommender=KorenImplicitKNN --recommender-options="num_factors=5"
--data-dir=data/ml-100k --find-iter=1

which is not particularly good.

IntegratedSVDPlusPlusKNN seems to converge nicely.

Did you calibrate the methods against what was reported in the paper?

—
Reply to this email directly or view it on GitHubhttps://github.com//pull/376#issuecomment-12718590.

---

Prof. Marcelo G. Manzato
Computer Science Department (SCC)
Mathematics and Computer Science Institute (ICMC)
University of Sao Paulo (USP)
Sao Carlos-SP Brazil

+55 16 3373 6638

---

Prof. Marcelo G. Manzato
Computer Science Department (SCC)
Mathematics and Computer Science Institute (ICMC)
University of Sao Paulo (USP)
Sao Carlos-SP Brazil

+55 16 3373 6601

[zenogantner]

Hi Marcelo,

thank you for the update.

Could you reproduce the results reported in the paper?
The reason I ask is because simpler methods have better results than 0.94 on ml-100k.

[mmanzato]

Hi Zeno,

I could try, but I checked that Koren used the quiz set (from netflix) to
test his algorithm. I do have the Netflix dataset which I downloaded from a
non-official website, but it doesn't include the quiz set, which I can't
find anywhere to download. Do you have it?

Alternatively, I could use only the training set, but in this case, I would
need to split it into training and test sets.

What do you think?

Thanks!
Marcelo

On Sat, Mar 16, 2013 at 4:44 PM, Zeno Gantner notifications@github.comwrote:

Hi Marcelo,

thank you for the update.

Could you reproduce the results reported in the paper?

—
Reply to this email directly or view it on GitHubhttps://github.com//pull/376#issuecomment-15010977
.

---

Prof. Marcelo G. Manzato
Computer Science Department (SCC)
Mathematics and Computer Science Institute (ICMC)
University of Sao Paulo (USP)
Sao Carlos-SP Brazil

+55 16 3373 6601