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Tavsiye Sistemleri, Derin Öğrenme (Deep Recommender Systems)

Kullanıcı $i$ için film $j$ tavsiye problemi için bir yaklaşım, eğitim verisinde filmlere verilen beğeni notlarını (ratings) bir $i,j$ matrisinde tutmak, SVD ile bu matris üzerinde ayrıştırma yaparak hem kullanıcı hem film boyutlarında azaltılmış temsili vektörler elde etmek, ardından bu vektörleri mesela kullanıcı $i$ vektörü ile tüm filmlerin azaltılmış temsildeki vektörleri ile çarpmak suretiyle o filmlere (çoğunlukla daha verilmemiş) notun ne olacağını tahmin etmeye uğraşmaktı.

Derin öğrenme, TensorFlow ile çözüm ne olabilir? Aslında üstteki ayrıştırma problemini TF ile çözmek mümkün, çünkü elde bir çarpım var, eğitim verisi var, çarpımın (tahmin) gerçek veriden olan farkı bir hata fonksiyonu olarak görülebilir, ve bu hatanın TF ile otomatik türevi alınarak minimizasyon noktası aranabilir.

Fakat tavsiye üretme mekanizmasını pür derin YSA bakışıyla çözmek belki daha iyi. Doğal dil işlemede (NLP) görülen kelime gömme (word embedding) kavramını bu problemde de kullanabiliriz. NLP problemlerinde elde bir doküman vardır, bir sözlükten gelen kelimeler bu belgelerde görülebilir. Kullanıcı / film, ya da müşteri / ürün bağlamında da sanki bir doküman vardır, ama bu dokümanda sadece tek bir beğeni için olan tek bir müşteri / ürün ikilisi vardır. NLP'de bir belge " kedi paspasta oturuyor", kimlik verisi üzerinden [200, 201, 565] olabiliyordu, kullanıcı / film için "belgede" mesela sadece [100, 4500] olur; kullanıcı 100 film 4500'i beğenmiş. Beğeni notunun nereye gittiğinin birazdan göreceğiz.

O zaman alttaki gibi bir ağ yapısı düşünülebilir; kullanıcı / film ikilisi kimlik verilerine gömme transformasyonu uygulanır, ve sonuç gömme vektörleri bu tabakada yanyana yer alırlar. Ardından gömme tabakasındaki tüm bilgi tam bağlanmış (fully connected) iki ayrı tabaka üzerinden tahmin üretmek için kullanılır, bu tahmin ise beğeni notunun ta kendisidir. Eldeki eğitim verisinde gerçek verilen not olacaktır, ve bir kayıp fonksiyonu üzerinden optimizasyon yapılır.

Meta Veri

Bu mimariye bazı ekler yapabiliriz. Mesela eğer müşteri, ürün hakkında "meta" bilgiler var ise, diyelim kullanıcıların yaşı, mesleği gibi, bu bilgiler de eğitim sırasında kullanılabilir. Kullanıcı meta bilgisi aynı kullanıcı için bir beğeniden diğerine değişmezler, o sebeple ürünler için ürünün hangi tarihte üretildiği, fiyatı, vs. gibi detaylar meta olabilir. Peki bu bilgileri nasıl DYSA'ya dahil ederiz?

Meta bilgisini direk gömme tabakasında sayısal kolonların hemen yanına koyabiliriz, mesela ürünün yapılış tarihi tek bir sayısal kolon olarak bu tabakada yer alabilir, yaş, vs. aynı şekilde. Mimari olarak, müşteri $i$ ve ürün $j$ için meta veri $m_i,n_j$ olsun,

şeklinde olur.

Kodlamaya başlayabiliriz. Önce verileri okuyalım, eğitim test verisi yaratalım (DYSA paketi olarak TensorFlow üzerinde yazılmış onu biraz daha kolaylaştıran Keras paketi kullanıldı [1])

import matplotlib.pyplot as plt
from pathlib import Path
from zipfile import ZipFile
from keras.layers import Input, Embedding, Flatten, merge, Dense, Dropout, Lambda
from keras.models import Model
import keras.backend as K
import pandas as pd, zipfile
from sklearn.metrics import mean_squared_error, mean_absolute_error

ML_100K_FOLDER = Path('ml-100k')
icols = ["name", "date", "genre", "url"]
icols += ["f" + str(x) for x in range(19)]  # unused feature names
rcols  = ["user_id", "item_id", "rating", "timestamp"]
with zipfile.ZipFile('ml-100k.zip', 'r') as z:
    all_ratings = pd.read_csv(z.open('ml-100k/u.data'), sep='\t', names=rcols)
    items = pd.read_csv(z.open('ml-100k/u.item'), sep='|',names=icols, encoding='latin-1')

items.fillna(value="01-Jan-1997", inplace=True)

from sklearn.model_selection import train_test_split

ratings_train, ratings_test = train_test_split(
    all_ratings, test_size=0.2, random_state=0)

print ratings_train.shape

(80000, 4)

Şimdi gömme tabakası kurulur, kullanıcı, film için bu 32'şer boyutlu olsun, yani yanyana 64 öğeli bir vektör. 

user_id_input = Input(shape=[1])
item_id_input = Input(shape=[1])
meta_input = Input(shape=[1])

user_id_train = ratings_train['user_id']
item_id_train = ratings_train['item_id']
rating_train = ratings_train['rating']

user_id_test = ratings_test['user_id']
item_id_test = ratings_test['item_id']
rating_test = ratings_test['rating']

max_user_id = all_ratings['user_id'].max()
max_item_id = all_ratings['item_id'].max()

embedding_size = 32
user_embedding = Embedding(output_dim=embedding_size, input_dim=max_user_id + 1,
                           input_length=1)(user_id_input)
item_embedding = Embedding(output_dim=embedding_size, input_dim=max_item_id + 1,
                           input_length=1)(item_id_input)

user_vecs = Flatten()(user_embedding)

item_vecs = Flatten()(item_embedding)

Şimdi bir meta kolonu ekleyelim, bunu sadece filmin üretim tarihi için yapacağız, fakat diğerleri için de yapılacak ek aynen bu şekilde olurdu,


parsed_dates = [int(film_date[-4:])
                for film_date in items["date"].tolist()]

items['parsed_date'] = pd.Series(parsed_dates, index=items.index)
max_date = max(items['parsed_date'])
min_date = min(items['parsed_date'])

from sklearn.preprocessing import scale

items['scaled_date'] = scale(items['parsed_date'].astype('float64'))
item_meta_train = items["scaled_date"][item_id_train]
item_meta_test = items["scaled_date"][item_id_test]

input_vecs = merge([user_vecs, item_vecs, meta_input], mode='concat')

Şimdi tam bağlanmış tabakaları ekleyelim, arada bir de veri atma (dropout) tabakası olsun, bu tabaka içinden geçen verilerin bazılarını / rasgele bir şekilde yoksayıyor (eğitim döngüsünün o aşaması yapılmıyor yani), bu tekniğin aşırı uygunluk (overfitting) problemleri için faydalı olduğu keşfedildi.

Tabakalar hazırlanınca eğitime başlayabiliriz,

x = Dense(64, activation='relu')(input_vecs)
x = Dropout(0.5)(x)
x = Dense(32, activation='relu')(x)
y = Dense(1)(x)

model = Model(input=[user_id_input, item_id_input, meta_input], output=y)
model.compile(optimizer='adam', loss='mae')

initial_train_preds = model.predict([user_id_train, item_id_train, item_meta_train])

history = model.fit([user_id_train, item_id_train, item_meta_train], rating_train,
                    batch_size=64, nb_epoch=15, validation_split=0.1,
                    shuffle=True)

test_preds = model.predict([user_id_test, item_id_test, item_meta_test])
print("Final test Loss: %0.3f" % mean_squared_error(test_preds, rating_test))
print("Final test Loss: %0.3f" % mean_absolute_error(test_preds, rating_test))

Train on 72000 samples, validate on 8000 samples
Epoch 1/15
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Epoch 2/15
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Epoch 3/15
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10s - loss: 0.7361 - val_loss: 0.7489
Epoch 4/15
   64/72000 [..............................] - ETA: 15s - loss: 0.7283  512/72000 [..............................] - ETA: 9s - loss: 0.6937  1024/72000 [..............................] - ETA: 8s - loss: 0.7122 1472/72000 [..............................] - ETA: 8s - loss: 0.7083 1792/72000 [..............................] - ETA: 8s - loss: 0.7100 2304/72000 [..............................] - ETA: 8s - loss: 0.7168 2752/72000 [>.............................] - ETA: 8s - loss: 0.7202 3136/72000 [>.............................] - ETA: 8s - loss: 0.7247 3520/72000 [>.............................] - ETA: 8s - loss: 0.7232 3968/72000 [>.............................] - ETA: 8s - loss: 0.7231 4480/72000 [>.............................] - ETA: 8s - loss: 0.7239 4928/72000 [=>............................] - ETA: 8s - loss: 0.7201 5248/72000 [=>............................] - ETA: 8s - loss: 0.7164 5696/72000 [=>............................] - ETA: 8s - loss: 0.7119 6080/72000 [=>............................] - 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Epoch 5/15
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Epoch 6/15
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Epoch 7/15
   64/72000 [..............................] - ETA: 16s - loss: 0.6908  384/72000 [..............................] - ETA: 12s - loss: 0.6640  768/72000 [..............................] - ETA: 11s - loss: 0.6748 1216/72000 [..............................] - ETA: 10s - loss: 0.6751 1728/72000 [..............................] - ETA: 9s - loss: 0.6714  2240/72000 [..............................] - ETA: 8s - loss: 0.6753 2752/72000 [>.............................] - ETA: 8s - loss: 0.6793 3136/72000 [>.............................] - ETA: 8s - loss: 0.6740 3520/72000 [>.............................] - ETA: 9s - loss: 0.6707 3904/72000 [>.............................] - ETA: 9s - loss: 0.6656 4288/72000 [>.............................] - ETA: 9s - loss: 0.6652 4672/72000 [>.............................] - ETA: 9s - loss: 0.6640 4992/72000 [=>............................] - ETA: 9s - loss: 0.6609 5440/72000 [=>............................] - ETA: 8s - loss: 0.6612 5952/72000 [=>............................] - 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Epoch 8/15
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Epoch 9/15
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Epoch 10/15
   64/72000 [..............................] - ETA: 18s - loss: 0.6458  512/72000 [..............................] - ETA: 10s - loss: 0.6474 1024/72000 [..............................] - ETA: 8s - loss: 0.6720  1408/72000 [..............................] - ETA: 8s - loss: 0.6647 1856/72000 [..............................] - ETA: 9s - loss: 0.6454 2240/72000 [..............................] - ETA: 9s - loss: 0.6513 2752/72000 [>.............................] - ETA: 8s - loss: 0.6519 3136/72000 [>.............................] - ETA: 8s - loss: 0.6532 3648/72000 [>.............................] - ETA: 8s - loss: 0.6585 3968/72000 [>.............................] - ETA: 8s - loss: 0.6564 4288/72000 [>.............................] - ETA: 9s - loss: 0.6578 4608/72000 [>.............................] - ETA: 9s - loss: 0.6543 4992/72000 [=>............................] - ETA: 9s - loss: 0.6549 5376/72000 [=>............................] - ETA: 9s - loss: 0.6473 5760/72000 [=>............................] - 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10s - loss: 0.6587 - val_loss: 0.7401
Epoch 11/15
   64/72000 [..............................] - ETA: 20s - loss: 0.7065  512/72000 [..............................] - ETA: 10s - loss: 0.6800 1024/72000 [..............................] - ETA: 8s - loss: 0.6801  1408/72000 [..............................] - ETA: 9s - loss: 0.6655 1728/72000 [..............................] - ETA: 9s - loss: 0.6598 2176/72000 [..............................] - ETA: 9s - loss: 0.6457 2624/72000 [>.............................] - ETA: 9s - loss: 0.6377 2944/72000 [>.............................] - ETA: 9s - loss: 0.6402 3392/72000 [>.............................] - ETA: 9s - loss: 0.6429 3840/72000 [>.............................] - ETA: 9s - loss: 0.6389 4288/72000 [>.............................] - ETA: 9s - loss: 0.6371 4672/72000 [>.............................] - ETA: 9s - loss: 0.6350 5120/72000 [=>............................] - ETA: 8s - loss: 0.6358 5440/72000 [=>............................] - ETA: 8s - loss: 0.6334 5824/72000 [=>............................] - 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Epoch 12/15
   64/72000 [..............................] - ETA: 19s - loss: 0.6589  384/72000 [..............................] - ETA: 13s - loss: 0.6311  704/72000 [..............................] - ETA: 12s - loss: 0.6303 1216/72000 [..............................] - ETA: 10s - loss: 0.6225 1536/72000 [..............................] - ETA: 11s - loss: 0.6275 1920/72000 [..............................] - ETA: 10s - loss: 0.6232 2304/72000 [..............................] - ETA: 10s - loss: 0.6228 2624/72000 [>.............................] - ETA: 10s - loss: 0.6233 3008/72000 [>.............................] - ETA: 10s - loss: 0.6238 3392/72000 [>.............................] - ETA: 10s - loss: 0.6227 3840/72000 [>.............................] - ETA: 10s - loss: 0.6295 4224/72000 [>.............................] - ETA: 10s - loss: 0.6260 4544/72000 [>.............................] - ETA: 10s - loss: 0.6302 5056/72000 [=>............................] - ETA: 9s - loss: 0.6305  5440/72000 [=>............................] - 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10s - loss: 0.6497 - val_loss: 0.7269
Epoch 13/15
   64/72000 [..............................] - ETA: 12s - loss: 0.5648  576/72000 [..............................] - ETA: 8s - loss: 0.6096   960/72000 [..............................] - ETA: 9s - loss: 0.6074 1344/72000 [..............................] - ETA: 9s - loss: 0.6195 1728/72000 [..............................] - ETA: 9s - loss: 0.6218 2048/72000 [..............................] - ETA: 9s - loss: 0.6332 2432/72000 [>.............................] - ETA: 9s - loss: 0.6359 2880/72000 [>.............................] - ETA: 9s - loss: 0.6380 3328/72000 [>.............................] - ETA: 9s - loss: 0.6379 3776/72000 [>.............................] - ETA: 8s - loss: 0.6395 4288/72000 [>.............................] - ETA: 8s - loss: 0.6390 4736/72000 [>.............................] - ETA: 8s - loss: 0.6342 5184/72000 [=>............................] - ETA: 8s - loss: 0.6353 5696/72000 [=>............................] - ETA: 8s - loss: 0.6339 6080/72000 [=>............................] - 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ETA: 0s - loss: 0.646671680/72000 [============================>.] - ETA: 0s - loss: 0.646272000/72000 [==============================] - 10s - loss: 0.6463 - val_loss: 0.7286
Epoch 14/15
   64/72000 [..............................] - ETA: 12s - loss: 0.6572  576/72000 [..............................] - ETA: 8s - loss: 0.5961   960/72000 [..............................] - ETA: 8s - loss: 0.6294 1216/72000 [..............................] - ETA: 10s - loss: 0.6260 1664/72000 [..............................] - ETA: 10s - loss: 0.6372 1984/72000 [..............................] - ETA: 10s - loss: 0.6432 2368/72000 [..............................] - ETA: 10s - loss: 0.6399 2816/72000 [>.............................] - ETA: 9s - loss: 0.6316  3264/72000 [>.............................] - ETA: 9s - loss: 0.6277 3776/72000 [>.............................] - ETA: 9s - loss: 0.6325 4224/72000 [>.............................] - ETA: 8s - loss: 0.6351 4672/72000 [>.............................] - ETA: 8s - loss: 0.6340 4992/72000 [=>............................] - ETA: 9s - loss: 0.6315 5312/72000 [=>............................] - ETA: 9s - loss: 0.6313 5696/72000 [=>............................] - 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Epoch 15/15
   64/72000 [..............................] - ETA: 12s - loss: 0.6015  384/72000 [..............................] - ETA: 11s - loss: 0.6559  704/72000 [..............................] - ETA: 11s - loss: 0.6175  960/72000 [..............................] - ETA: 12s - loss: 0.6058 1344/72000 [..............................] - ETA: 11s - loss: 0.6102 1728/72000 [..............................] - ETA: 11s - loss: 0.6171 2112/72000 [..............................] - ETA: 11s - loss: 0.6145 2432/72000 [>.............................] - ETA: 11s - loss: 0.6157 2752/72000 [>.............................] - ETA: 11s - loss: 0.6180 3136/72000 [>.............................] - ETA: 11s - loss: 0.6173 3584/72000 [>.............................] - ETA: 11s - loss: 0.6187 3904/72000 [>.............................] - ETA: 11s - loss: 0.6143 4288/72000 [>.............................] - ETA: 10s - loss: 0.6111 4736/72000 [>.............................] - ETA: 10s - loss: 0.6116 5120/72000 [=>............................] - 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10s - loss: 0.6395 - val_loss: 0.7293
Final test Loss: 0.968
Final test Loss: 0.719

Test verisi üzerinde hata karelerinin ortalaması (mean squared error) ve hata mutlak değer ortalaması (mean absolute error) görülüyor. Notların 1 ile 5 arasında olduğunu düşünürsek sonuçlar fena değil diyebiliriz.

Tavsiye Üretmek

Eğitilmiş model sayesinde artık kullanıcının hiç seyretmediği filmler için beğeni notu üretmek mümkün, herhangi bir kullanıcı / film kimliğini DYSA'ya soldan veriyoruz, sağdan tahmin notu çıkıyor, DYSA'nın her kullanıcı ve her film için elinde gömme temsili vektörleri var. O zaman tavsiye üretme amacıyla herhangi bir kullanıcı için tüm filmlerin tahmini notlarını hesaplarsak, ve sonuç beğeni notları içinde en üst 10 filmi seçersek bu filmler bu kullanıcının görüp beğenebileceği filmler olacaktır. Kullanıcı 100 için bu tahminleri üretelim,

def recommend(user_id, top_n=10):
    item_ids = range(1, max_item_id)
    seen_movies = list(all_ratings[all_ratings["user_id"]==user_id]["item_id"])
    item_ids = list(filter(lambda x: x not in seen_movies, item_ids))

    item_ids = np.array(item_ids)
    user = np.zeros_like(item_ids)
    user[:]=user_id
    items_meta = items["scaled_date"][item_ids].values

    rating_preds = model.predict([user, item_ids, items_meta])

    item_ids = np.argsort(rating_preds[:,0])[::-1].tolist()
    rec_items = item_ids[:top_n]
    return [(items["name"][movie], rating_preds[movie][0]) for movie in rec_items]

for x in recommend(100): 
    print x

(u'Nemesis 2: Nebula (1995)', 4.7475376)
(u'Vie est belle, La (Life is Rosey) (1987)', 4.6862297)
(u'Above the Rim (1994)', 4.6599483)
(u'Specialist, The (1994)', 4.6244154)
(u'Bushwhacked (1995)', 4.6114397)
(u'Six Degrees of Separation (1993)', 4.4849181)
(u'Prophecy, The (1995)', 4.43927)
(u'Innocent Sleep, The (1995)', 4.4117985)
(u'Legal Deceit (1997)', 4.3874221)
(u'Mondo (1996)', 4.3473449)

Bu arkadaş üstteki filmleri sevebilirmiş.

Yakınlık

Gömme tabakasında her kullanıcı, film daha az boyutta, sayısal değerlerle temsil edilir dedik, mesela Toy Story filmi için 

weights = model.get_weights()

user_embeddings = weights[0]
item_embeddings = weights[1]

print("First item name from metadata:", items["name"][1])
print("Embedding vector for the first item:")
print(item_embeddings[1])
print("shape:", item_embeddings[1].shape)

('First item name from metadata:', u'Toy Story (1995)')
Embedding vector for the first item:
[-0.15852147  0.1097799   0.16468367 -0.10575533  0.17060712  0.04193607
 -0.10610512  0.17894444 -0.0052576  -0.21044061  0.18178959 -0.12612586
 -0.11098795 -0.09544351  0.23231728  0.18473586 -0.08239312 -0.22889471
  0.16191281  0.12349296  0.10502452  0.14639899  0.06178252  0.12817873
 -0.15082233  0.06978928 -0.17052469 -0.09871896 -0.05171229  0.07913745
  0.16838557 -0.11518386]
('shape:', (32,))

Bu sayıların yakınlık bağlamında anlamı olduğundan bahsettik. O zaman bir filmin diğerlerine olan yakınlığını Öklitsel uzaklık ile hesaplayabiliriz, mesela Groundhog Day filmine yakın filmler hangileridir?

def euclidean_distances(x):
    return np.linalg.norm(item_embeddings - x, axis=1)

def most_similar(idx, top_n=10, mode='euclidean'):
    sorted_indexes=0
    dists = euclidean_distances(item_embeddings[idx])
    sorted_indexes = np.argsort(dists)
    idxs = sorted_indexes[0:top_n]
    return list(zip(items["name"][idxs], dists[idxs]))

euc_dists = euclidean_distances(item_embeddings[1])
print(euc_dists.shape)
print(euc_dists[1:5])
print()

print("Groundhog Day filmine yakin filmler:")
for title, dist in most_similar(202, mode="euclidean"):
    print(title, dist)

(1683,)
[ 0.          0.96932679  1.29504049  0.57039422]
()
Groundhog Day filmine yakin filmler:
(u'Groundhog Day (1993)', 0.0)
(u'Better Off Dead... (1985)', 0.33926776)
(u'Sleepers (1996)', 0.33967811)
(u'Dazed and Confused (1993)', 0.35498708)
(u'Butcher Boy, The (1998)', 0.35616043)
(u'Star Trek: First Contact (1996)', 0.3641389)
(u'Jane Eyre (1996)', 0.37474769)
(u'American Werewolf in London, An (1981)', 0.37788758)
(u'Grosse Pointe Blank (1997)', 0.39449736)
(u'Time to Kill, A (1996)', 0.39465994)

Pek anlamsız değil. Tabii girdi verisi ne kadar fazla olursa sonuçlar o kadar iyi olur, kullandığımız veri Movielens 100K verisi, 100,000 tane not var. MovieLens 1M (1 milyon not) ile daha iyi sonuçlar alınabilir (bu veri İstatistik, Pivotlama yazısının dizininde). Okur için ödev olsun.

Kaynaklar

[1] Grisel, Deep Learning, course @ Télécom ParisTech, https://github.com/petermartigny/Deep-Learning

[2] https://keras.io/examples/structured_data/collaborative_filtering_movielens/

[3] https://ogrisel.github.io/decks/2017_dotai_neural_recsys/#1


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