推薦電影：使用序列模型進行檢索

在本教學課程中，我們將建構序列檢索模型。序列推薦是一種熱門模型，會查看使用者先前互動過的一系列項目，然後預測下一個項目。由於每個序列中項目的順序很重要，因此我們將使用循環神經網路來建立序列關係模型。如需更多詳細資訊，請參閱這篇 GRU4Rec 論文。

匯入

首先，我們先處理好相依性和匯入。

pip install -q tensorflow-recommenders
pip install -q --upgrade tensorflow-datasets

import os
import pprint
import tempfile

from typing import Dict, Text

import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
import tensorflow_recommenders as tfrs

2022-12-14 12:39:47.708842: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2022-12-14 12:39:47.708940: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2022-12-14 12:39:47.708949: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.

準備資料集

接下來，我們需要準備資料集。我們將在這個 TensorFlow Lite 裝置端推薦參考應用程式中運用資料產生公用程式。

MovieLens 1M 資料包含 ratings.dat (欄：UserID、MovieID、Rating、Timestamp) 和 movies.dat (欄：MovieID、Title、Genres)。範例產生指令碼會下載 1M 資料集，並採用這兩個檔案，僅保留評分高於 2 的評分，形成使用者電影互動時間軸，將範例活動作為標籤，並將 10 個先前的使用者活動作為預測的情境。

wget -nc https://raw.githubusercontent.com/tensorflow/examples/master/lite/examples/recommendation/ml/data/example_generation_movielens.py
python -m example_generation_movielens  --data_dir=data/raw  --output_dir=data/examples  --min_timeline_length=3  --max_context_length=10  --max_context_movie_genre_length=10  --min_rating=2  --train_data_fraction=0.9  --build_vocabs=False

--2022-12-14 12:39:49--  https://raw.githubusercontent.com/tensorflow/examples/master/lite/examples/recommendation/ml/data/example_generation_movielens.py
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 18041 (18K) [text/plain]
Saving to: ‘example_generation_movielens.py’

example_generation_ 100%[===================>]  17.62K  --.-KB/s    in 0.001s  

2022-12-14 12:39:49 (18.6 MB/s) - ‘example_generation_movielens.py’ saved [18041/18041]

2022-12-14 12:39:50.711022: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2022-12-14 12:39:50.711113: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2022-12-14 12:39:50.711126: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
I1214 12:39:51.542600 139789676263232 example_generation_movielens.py:460] Downloading and extracting data.
Downloading data from https://files.grouplens.org/datasets/movielens/ml-1m.zip
1826816/5917549 [========>.....................] - ETA: 0s5917549/5917549 [==============================] - 0s 0us/step
I1214 12:39:52.073689 139789676263232 example_generation_movielens.py:406] Reading data to dataframes.
/tmpfs/src/temp/docs/examples/example_generation_movielens.py:132: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.
  ratings_df = pd.read_csv(
/tmpfs/src/temp/docs/examples/example_generation_movielens.py:140: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.
  movies_df = pd.read_csv(
I1214 12:39:56.795858 139789676263232 example_generation_movielens.py:408] Generating movie rating user timelines.
I1214 12:39:59.942978 139789676263232 example_generation_movielens.py:410] Generating train and test examples.
6040/6040 [==============================] - 72s 12ms/step
93799/93799 [==============================] - 2s 17us/step
I1214 12:41:38.911691 139789676263232 example_generation_movielens.py:473] Generated dataset: {'train_size': 844195, 'test_size': 93799, 'train_file': 'data/examples/train_movielens_1m.tfrecord', 'test_file': 'data/examples/test_movielens_1m.tfrecord'}

以下是產生資料集的範例。

0 : {
  features: {
    feature: {
      key  : "context_movie_id"
      value: { int64_list: { value: [ 1124, 2240, 3251, ..., 1268 ] } }
    }
    feature: {
      key  : "context_movie_rating"
      value: { float_list: {value: [ 3.0, 3.0, 4.0, ..., 3.0 ] } }
    }
    feature: {
      key  : "context_movie_year"
      value: { int64_list: { value: [ 1981, 1980, 1985, ..., 1990 ] } }
    }
    feature: {
      key  : "context_movie_genre"
      value: { bytes_list: { value: [ "Drama", "Drama", "Mystery", ..., "UNK" ] } }
    }
    feature: {
      key  : "label_movie_id"
      value: { int64_list: { value: [ 3252 ] }  }
    }
  }
}

您可以看到它包含情境電影 ID 序列和標籤電影 ID (下一部電影)，以及情境功能，例如電影年份、評分和類型。

在我們的案例中，我們只會使用情境電影 ID 序列和標籤電影 ID。您可以參考善用情境功能教學課程，以深入瞭解新增其他情境功能。

train_filename = "./data/examples/train_movielens_1m.tfrecord"
train = tf.data.TFRecordDataset(train_filename)

test_filename = "./data/examples/test_movielens_1m.tfrecord"
test = tf.data.TFRecordDataset(test_filename)

feature_description = {
    'context_movie_id': tf.io.FixedLenFeature([10], tf.int64, default_value=np.repeat(0, 10)),
    'context_movie_rating': tf.io.FixedLenFeature([10], tf.float32, default_value=np.repeat(0, 10)),
    'context_movie_year': tf.io.FixedLenFeature([10], tf.int64, default_value=np.repeat(1980, 10)),
    'context_movie_genre': tf.io.FixedLenFeature([10], tf.string, default_value=np.repeat("Drama", 10)),
    'label_movie_id': tf.io.FixedLenFeature([1], tf.int64, default_value=0),
}

def _parse_function(example_proto):
  return tf.io.parse_single_example(example_proto, feature_description)

train_ds = train.map(_parse_function).map(lambda x: {
    "context_movie_id": tf.strings.as_string(x["context_movie_id"]),
    "label_movie_id": tf.strings.as_string(x["label_movie_id"])
})

test_ds = test.map(_parse_function).map(lambda x: {
    "context_movie_id": tf.strings.as_string(x["context_movie_id"]),
    "label_movie_id": tf.strings.as_string(x["label_movie_id"])
})

for x in train_ds.take(1).as_numpy_iterator():
  pprint.pprint(x)

WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.
Instructions for updating:
Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089
{'context_movie_id': array([b'908', b'1086', b'1252', b'2871', b'3551', b'593', b'247', b'608',
       b'1358', b'866'], dtype=object),
 'label_movie_id': array([b'190'], dtype=object)}

現在我們的訓練/測試資料集僅包含歷史電影 ID 序列和下部電影 ID 的標籤。請注意，我們在 tf.Example 剖析期間使用 [10] 作為功能的形狀，因為我們在範例產生步驟中指定 10 作為情境功能的長度。

在開始建構模型之前，我們還需要一件事 - 電影 ID 的詞彙表。

movies = tfds.load("movielens/1m-movies", split='train')
movies = movies.map(lambda x: x["movie_id"])
movie_ids = movies.batch(1_000)
unique_movie_ids = np.unique(np.concatenate(list(movie_ids)))

實作序列模型

在我們的基本檢索教學課程中，我們對使用者使用一個查詢塔，並對候選電影使用候選塔。但是，雙塔架構是可概括的，並且不限於配對。您也可以使用它來執行項目到項目的推薦，如我們在基本檢索教學課程中所述。

在這裡，我們仍然會使用雙塔架構。具體來說，我們將查詢塔與閘道循環單元 (GRU) 層搭配使用，以編碼歷史電影序列，並為候選電影保留相同的候選塔。

embedding_dimension = 32

query_model = tf.keras.Sequential([
    tf.keras.layers.StringLookup(
      vocabulary=unique_movie_ids, mask_token=None),
    tf.keras.layers.Embedding(len(unique_movie_ids) + 1, embedding_dimension), 
    tf.keras.layers.GRU(embedding_dimension),
])

candidate_model = tf.keras.Sequential([
  tf.keras.layers.StringLookup(
      vocabulary=unique_movie_ids, mask_token=None),
  tf.keras.layers.Embedding(len(unique_movie_ids) + 1, embedding_dimension)
])

指標、任務和完整模型的定義與基本檢索模型類似。

metrics = tfrs.metrics.FactorizedTopK(
  candidates=movies.batch(128).map(candidate_model)
)

task = tfrs.tasks.Retrieval(
  metrics=metrics
)

class Model(tfrs.Model):

    def __init__(self, query_model, candidate_model):
        super().__init__()
        self._query_model = query_model
        self._candidate_model = candidate_model

        self._task = task

    def compute_loss(self, features, training=False):
        watch_history = features["context_movie_id"]
        watch_next_label = features["label_movie_id"]

        query_embedding = self._query_model(watch_history)       
        candidate_embedding = self._candidate_model(watch_next_label)

        return self._task(query_embedding, candidate_embedding, compute_metrics=not training)

擬合和評估

我們現在可以編譯、訓練和評估我們的序列檢索模型。

model = Model(query_model, candidate_model)
model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.1))

cached_train = train_ds.shuffle(10_000).batch(12800).cache()
cached_test = test_ds.batch(2560).cache()

model.fit(cached_train, epochs=3)

Epoch 1/3
67/67 [==============================] - 18s 220ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_5_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_10_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_50_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_100_categorical_accuracy: 0.0000e+00 - loss: 108359.6299 - regularization_loss: 0.0000e+00 - total_loss: 108359.6299
Epoch 2/3
67/67 [==============================] - 3s 38ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_5_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_10_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_50_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_100_categorical_accuracy: 0.0000e+00 - loss: 101734.1007 - regularization_loss: 0.0000e+00 - total_loss: 101734.1007
Epoch 3/3
67/67 [==============================] - 3s 38ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_5_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_10_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_50_categorical_accuracy: 0.0000e+00 - factorized_top_k/top_100_categorical_accuracy: 0.0000e+00 - loss: 99763.0675 - regularization_loss: 0.0000e+00 - total_loss: 99763.0675
<keras.callbacks.History at 0x7fe89c26edf0>

model.evaluate(cached_test, return_dict=True)

37/37 [==============================] - 10s 221ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0144 - factorized_top_k/top_5_categorical_accuracy: 0.0775 - factorized_top_k/top_10_categorical_accuracy: 0.1347 - factorized_top_k/top_50_categorical_accuracy: 0.3712 - factorized_top_k/top_100_categorical_accuracy: 0.5030 - loss: 15530.8248 - regularization_loss: 0.0000e+00 - total_loss: 15530.8248
{'factorized_top_k/top_1_categorical_accuracy': 0.014403138309717178,
 'factorized_top_k/top_5_categorical_accuracy': 0.07749549299478531,
 'factorized_top_k/top_10_categorical_accuracy': 0.13472424447536469,
 'factorized_top_k/top_50_categorical_accuracy': 0.37120863795280457,
 'factorized_top_k/top_100_categorical_accuracy': 0.5029690861701965,
 'loss': 9413.7470703125,
 'regularization_loss': 0,
 'total_loss': 9413.7470703125}

本序列檢索教學課程到此結束。