Hello there! The following is the prompt and the answer for learning ad conversion rate (CVR) model.
Prompt: Can you generate an example list of features for ads conversion rate model? And also classify them into different categories
Sure! I'll provide you with an example list of features for an ads conversion rate model and categorize them into different groups.
-
Ad Attributes:
- Ad creative: Type of ad creative used (image, video, carousel, etc.), visual style, and message.
- Ad placement: Location of the ad display (news feed, right column, stories, search results, etc.).
- Ad frequency: Number of times a user has seen a specific ad or similar ads.
- Ad relevance score: A metric representing the ad's relevance to the user.
- Ad bid price: Cost per click or cost per impression for the ad.
- Call-to-action: Specific call-to-action used in the ad (e.g., "Shop Now," "Learn More," "Sign Up," etc.).
- Social proof: Presence of social proof elements in the ad (testimonials, reviews, number of followers).
-
User Attributes:
- Demographics: Age, gender, location, education, and income level of the user.
- Interests: Hobbies, preferences, and topics the user engages with online.
- Online behavior: Browsing history, time spent on websites, and frequency of visiting certain types of websites.
-
Technical Attributes:
- Device type: Type of device used by the user (mobile, desktop, tablet, etc.).
- Operating system: User's operating system (iOS, Android, Windows, macOS, etc.).
- Browser: User's web browser (Chrome, Safari, Firefox, Edge, etc.).
-
Temporal Attributes:
- Day of the week: The day on which the ad is displayed.
- Time of day: The time at which the ad is displayed (morning, afternoon, evening, etc.).
- Seasonality: Time of year (holiday season, summer, etc.) which may impact user behavior and conversion rates.
-
Landing Page Attributes:
- Landing page quality: Design, user experience, and relevance of the landing page to which the ad directs users.
These categories and features should provide a solid foundation for building an ads conversion rate model. Remember that the specific features you include will depend on the platform, type of ads you're analyzing, and the data available to you.
Prompt: Can you create a deep neural network model to predict conversion rate, with all of the above features, and with tensorflow code? Please also include the preprocessing step for the data, and create sample data to include all of the above features. Please also use different methods to process numerical features, categorical features and sequence features.
Certainly! Here's an example of a deep neural network model using TensorFlow to predict conversion rates based on the provided features. I'll include preprocessing steps for the data and create sample data to cover all of the features. I'll use different methods to process numerical, categorical, and sequence features.
First, let's import the necessary libraries:
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelEncoder
Now, let's create a sample dataset with the features discussed:
data = {
"ad_creative": ["image", "video", "image", "carousel", "image"],
"ad_placement": ["news_feed", "stories", "right_column", "news_feed", "search_results"],
"ad_frequency": [3, 1, 5, 2, 4],
"ad_relevance_score": [7.5, 6.3, 8.9, 5.5, 7.2],
"ad_bid_price": [0.5, 0.7, 0.4, 0.6, 0.5],
"call_to_action": ["shop_now", "learn_more", "shop_now", "sign_up", "shop_now"],
"social_proof": [True, False, True, False, True],
"age": [25, 34, 45, 29, 38],
"gender": ["M", "F", "M", "F", "M"],
"location": ["US", "CA", "UK", "AU", "US"],
"education": ["bachelor", "master", "high_school", "bachelor", "phd"],
"income_level": ["medium", "high", "low", "medium", "high"],
"interests": [
"sports,travel",
"cooking,gardening",
"technology,movies",
"travel,photography",
"sports,gaming",
],
"device_type": ["mobile", "desktop", "mobile", "tablet", "mobile"],
"operating_system": ["iOS", "Windows", "Android", "macOS", "iOS"],
"browser": ["Safari", "Edge", "Chrome", "Safari", "Chrome"],
"day_of_week": [1, 3, 6, 2, 5],
"time_of_day": ["morning", "afternoon", "evening", "morning", "afternoon"],
"season": ["summer", "winter", "spring", "fall", "summer"],
"landing_page_quality": [7.2, 6.8, 8.5, 7.1, 7.9],
"conversion_rate": [0.15, 0.08, 0.22, 0.11, 0.19],
}
df = pd.DataFrame(data)
Next, we'll preprocess the data, handling numerical, categorical, and sequence features differently:
# Numerical features
num_features = ["ad_frequency", "ad_relevance_score", "ad_bid_price", "age", "landing_page_quality"]
scaler = StandardScaler()
df[num_features] = scaler.fit_transform(df[num_features])
# Categorical features
cat_features = [
"ad_creative",
"ad_placement",
"call_to_action",
"gender",
"location",
"education",
"income_level",
"device_type",
"operating_system",
"browser",
please continue to generate the code. If the code is in python, please keep the format in code style.
"time_of_day",
"season",
]
for feature in cat_features:
le = LabelEncoder()
df[feature] = le.fit_transform(df[feature])
# Sequence features (interests)
interests = df["interests"].apply(lambda x: x.split(","))
# Tokenize and pad interests
tokenizer = tf.keras.preprocessing.text.Tokenizer()
tokenizer.fit_on_texts(interests)
sequences = tokenizer.texts_to_sequences(interests)
padded_interests = tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen=5)
# Convert social_proof to binary
df["social_proof"] = df["social_proof"].astype(int)
# Split the data into train and test sets
X = df.drop("conversion_rate", axis=1)
y = df["conversion_rate"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Separate preprocessed interests from the rest of the data
X_train_interests = padded_interests[X_train.index]
X_test_interests = padded_interests[X_test.index]
X_train = X_train.drop("interests", axis=1)
X_test = X_test.drop("interests", axis=1)
Now let's create the deep neural network model:
# Input layers
input_main = tf.keras.layers.Input(shape=(X_train.shape[1],), name="main_input")
input_interests = tf.keras.layers.Input(shape=(X_train_interests.shape[1],), name="interests_input")
# Embedding for interests
embedding = tf.keras.layers.Embedding(input_dim=len(tokenizer.word_index) + 1, output_dim=8)(input_interests)
embedding_flat = tf.keras.layers.Flatten()(embedding)
# Concatenate the main input and the flattened embedding
concat = tf.keras.layers.Concatenate()([input_main, embedding_flat])
# Hidden layers
dense1 = tf.keras.layers.Dense(128, activation="relu")(concat)
dropout1 = tf.keras.layers.Dropout(0.2)(dense1)
dense2 = tf.keras.layers.Dense(64, activation="relu")(dropout1)
dropout2 = tf.keras.layers.Dropout(0.2)(dense2)
# Output layer
output = tf.keras.layers.Dense(1, activation="linear")(dropout2)
# Create the model
model = tf.keras.models.Model(inputs=[input_main, input_interests], outputs=output)
# Compile the model
model.compile(optimizer="adam", loss="mse", metrics=["mae"])
# Display the model summary
model.summary()
Finally, let's train the model and evaluate it:
# Train the model
model.fit(
[X_train, X_train_interests],
y_train,
batch_size=32,
epochs=100,
validation_split=0.1,
verbose=1,
)
# Evaluate the model
loss, mae = model.evaluate([X_test, X_test_interests], y_test)
print(f"Mean Absolute Error: {mae:.4f}")
This code demonstrates how to preprocess and create a deep neural network model using TensorFlow to predict conversion rates based on the provided features. It uses different methods to process numerical, categorical, and sequence features.
Yes, there are several other methods for processing categorical features. Two popular methods are One-Hot Encoding and Target Encoding.
One-Hot Encoding: One-Hot Encoding is a technique that creates binary columns (0 or 1) for each category in a categorical feature. It results in a sparse matrix where each row has only one 1 in the new columns, representing the presence of a specific category. One-Hot Encoding is commonly used in machine learning algorithms when the categorical variables do not have any ordinal relationship. Here's an example of how to apply one-hot encoding using pandas:
# One-Hot Encoding
df_one_hot = pd.get_dummies(df, columns=cat_features)
Target Encoding (also known as Mean Encoding): Target Encoding is a method where each category is replaced by the mean of the target variable for that category. It's especially useful when dealing with high cardinality categorical features, as it creates fewer new features than one-hot encoding. However, you should be careful to avoid leakage when using target encoding. One way to prevent leakage is by performing target encoding separately for the training and testing datasets. Here's an example of applying target encoding using the category_encoders library:
!pip install category_encoders
import category_encoders as ce
# Split the data into train and test sets
X = df.drop("conversion_rate", axis=1)
y = df["conversion_rate"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Target Encoding
encoder = ce.TargetEncoder(cols=cat_features)
encoder.fit(X_train, y_train)
X_train_encoded = encoder.transform(X_train)
X_test_encoded = encoder.transform(X_test)
Remember to install the category_encoders library before using it. Each of these encoding methods has its advantages and disadvantages, and the choice of method depends on the specific dataset and problem you're working on.
For the sequence feature (interests), I used Tokenization and Padding as the processing methods. Tokenization is the process of converting a sequence of text into individual tokens (words or terms), and Padding ensures that all sequences have the same length by adding zeros to the shorter sequences.
Here's the relevant code snippet for processing the sequence feature:
# Sequence features (interests)
interests = df["interests"].apply(lambda x: x.split(","))
# Tokenize and pad interests
tokenizer = tf.keras.preprocessing.text.Tokenizer()
tokenizer.fit_on_texts(interests)
sequences = tokenizer.texts_to_sequences(interests)
padded_interests = tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen=5)
In this code, we first split the interests by commas to create a list of interests for each row. Then, we create a Tokenizer object from the tf.keras.preprocessing.text module and fit it on the interests data. The texts_to_sequences method converts the text data into sequences of integer tokens. Finally, we use the pad_sequences function from tf.keras.preprocessing.sequence to pad the token sequences, ensuring they all have the same length (in this case, a maximum length of 5).
These preprocessed sequences can then be fed into an Embedding layer in the neural network model to learn meaningful representations of the sequence data.
Yes, there are other ways to process sequence features, mainly depending on the problem and the structure of the data. Some popular methods include:
Word Embeddings (e.g., Word2Vec, GloVe, or FastText): Instead of learning embeddings from the data itself, you can use pre-trained word embeddings like Word2Vec, GloVe, or FastText. These embeddings are trained on large corpora and can capture semantic relationships between words. You can use these pre-trained embeddings as the weights for the Embedding layer in your neural network model.
Term Frequency-Inverse Document Frequency (TF-IDF): TF-IDF is a numerical statistic that is used to measure the importance of a word in a document or a collection of documents. It considers the frequency of the word in the document and its rarity across documents. You can use the TfidfVectorizer from the sklearn.feature_extraction.text module to transform the sequence data into a sparse matrix of TF-IDF features.
Bag of Words (BoW): Bag of Words is a technique where you represent text data as a "bag" (multiset) of words, disregarding grammar and word order but keeping track of frequency. You can use the CountVectorizer from the sklearn.feature_extraction.text module to transform the sequence data into a sparse matrix of word frequencies.
Using RNNs (Recurrent Neural Networks) or LSTMs (Long Short-Term Memory) layers: Instead of using the Embedding layer followed by a Flatten layer in your neural network model, you can use RNNs or LSTMs to process sequence data. These layers can handle varying sequence lengths and are capable of capturing long-term dependencies in the data. RNNs and LSTMs can be added to your model after the Embedding layer.
The choice of method depends on the specific problem, dataset, and requirements of your model. Each method has its advantages and disadvantages, and a combination of methods may also be used to achieve the best results.
Sure! Let's modify the neural network model to process sequence features using an LSTM layer instead of the Flatten layer after the Embedding layer. Here's the updated model code:
# Input layers
input_main = tf.keras.layers.Input(shape=(X_train.shape[1],), name="main_input")
input_interests = tf.keras.layers.Input(shape=(X_train_interests.shape[1],), name="interests_input")
# Embedding for interests
embedding = tf.keras.layers.Embedding(input_dim=len(tokenizer.word_index) + 1, output_dim=8)(input_interests)
# LSTM layer for interests
lstm = tf.keras.layers.LSTM(16)(embedding)
# Concatenate the main input and the LSTM output
concat = tf.keras.layers.Concatenate()([input_main, lstm])
# Hidden layers
dense1 = tf.keras.layers.Dense(128, activation="relu")(concat)
dropout1 = tf.keras.layers.Dropout(0.2)(dense1)
dense2 = tf.keras.layers.Dense(64, activation="relu")(dropout1)
dropout2 = tf.keras.layers.Dropout(0.2)(dense2)
# Output layer
output = tf.keras.layers.Dense(1, activation="linear")(dropout2)
# Create the model
model = tf.keras.models.Model(inputs=[input_main, input_interests], outputs=output)
# Compile the model
model.compile(optimizer="adam", loss="mse", metrics=["mae"])
# Display the model summary
model.summary()
In this updated model, we replaced the Flatten layer with an LSTM layer. The LSTM layer has 16 units and processes the output of the Embedding layer. The output of the LSTM layer is then concatenated with the main input and fed into the rest of the neural network.
You can train and evaluate this updated model using the same code as before:
# Train the model
model.fit(
[X_train, X_train_interests],
y_train,
batch_size=32,
epochs=100,
validation_split=0.1,
verbose=1,
)
# Evaluate the model
loss, mae = model.evaluate([X_test, X_test_interests], y_test)
print(f"Mean Absolute Error: {mae:.4f}")
This model uses an LSTM layer to process the sequence features, allowing it to capture dependencies in the data and potentially improve the overall performance of the model.
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelEncoder
data = {
"ad_creative": ["image", "video", "image", "carousel", "image"],
"ad_placement": ["news_feed", "stories", "right_column", "news_feed", "search_results"],
"ad_frequency": [3, 1, 5, 2, 4],
"ad_relevance_score": [7.5, 6.3, 8.9, 5.5, 7.2],
"ad_bid_price": [0.5, 0.7, 0.4, 0.6, 0.5],
"call_to_action": ["shop_now", "learn_more", "shop_now", "sign_up", "shop_now"],
"social_proof": [True, False, True, False, True],
"age": [25, 34, 45, 29, 38],
"gender": ["M", "F", "M", "F", "M"],
"location": ["US", "CA", "UK", "AU", "US"],
"education": ["bachelor", "master", "high_school", "bachelor", "phd"],
"income_level": ["medium", "high", "low", "medium", "high"],
"interests": [
"sports,travel",
"cooking,gardening",
"technology,movies",
"travel,photography",
"sports,gaming",
],
"device_type": ["mobile", "desktop", "mobile", "tablet", "mobile"],
"operating_system": ["iOS", "Windows", "Android", "macOS", "iOS"],
"browser": ["Safari", "Edge", "Chrome", "Safari", "Chrome"],
"day_of_week": [1, 3, 6, 2, 5],
"time_of_day": ["morning", "afternoon", "evening", "morning", "afternoon"],
"season": ["summer", "winter", "spring", "fall", "summer"],
"landing_page_quality": [7.2, 6.8, 8.5, 7.1, 7.9],
"conversion_rate": [0.15, 0.08, 0.22, 0.11, 0.19],
}
df = pd.DataFrame(data)
# Numerical features
num_features = ["ad_frequency", "ad_relevance_score", "ad_bid_price", "age", "landing_page_quality"]
scaler = StandardScaler()
df[num_features] = scaler.fit_transform(df[num_features])
# Categorical features
cat_features = [
"ad_creative",
"ad_placement",
"call_to_action",
"gender",
"location",
"education",
"income_level",
"device_type",
"operating_system",
"browser",
"time_of_day",
"season",
]
for feature in cat_features:
le = LabelEncoder()
df[feature] = le.fit_transform(df[feature])
# Sequence features (interests)
interests = df["interests"].apply(lambda x: x.split(","))
# Tokenize and pad interests
tokenizer = tf.keras.preprocessing.text.Tokenizer()
tokenizer.fit_on_texts(interests)
sequences = tokenizer.texts_to_sequences(interests)
padded_interests = tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen=5)
# Convert social_proof to binary
df["social_proof"] = df["social_proof"].astype(int)
# Split the data into train and test sets
X = df.drop("conversion_rate", axis=1)
y = df["conversion_rate"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Separate preprocessed interests from the rest of the data
X_train_interests = padded_interests[X_train.index]
X_test_interests = padded_interests[X_test.index]
X_train = X_train.drop("interests", axis=1)
X_test = X_test.drop("interests", axis=1)
# Input layers
input_main = tf.keras.layers.Input(shape=(X_train.shape[1],), name="main_input")
input_interests = tf.keras.layers.Input(shape=(X_train_interests.shape[1],), name="interests_input")
# Embedding for interests
embedding = tf.keras.layers.Embedding(input_dim=len(tokenizer.word_index) + 1, output_dim=8)(input_interests)
embedding_flat = tf.keras.layers.Flatten()(embedding)
# Concatenate the main input and the flattened embedding
concat = tf.keras.layers.Concatenate()([input_main, embedding_flat])
# Hidden layers
dense1 = tf.keras.layers.Dense(128, activation="relu")(concat)
dropout1 = tf.keras.layers.Dropout(0.2)(dense1)
dense2 = tf.keras.layers.Dense(64, activation="relu")(dropout1)
dropout2 = tf.keras.layers.Dropout(0.2)(dense2)
# Output layer
output = tf.keras.layers.Dense(1, activation="linear")(dropout2)
# Create the model
model = tf.keras.models.Model(inputs=[input_main, input_interests], outputs=output)
# Compile the model
model.compile(optimizer="adam", loss="mse", metrics=["mae"])
# Display the model summary
model.summary()
# Train the model
model.fit(
[X_train, X_train_interests],
y_train,
batch_size=32,
epochs=10,
validation_split=0.1,
verbose=1,
)
# Evaluate the model
loss, mae = model.evaluate([X_test, X_test_interests], y_test)
print(f"Mean Absolute Error: {mae:.4f}")
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
interests_input (InputLayer) [(None, 5)] 0 []
embedding_1 (Embedding) (None, 5, 8) 72 ['interests_input[0][0]']
main_input (InputLayer) [(None, 19)] 0 []
flatten_1 (Flatten) (None, 40) 0 ['embedding_1[0][0]']
concatenate_1 (Concatenate) (None, 59) 0 ['main_input[0][0]',
'flatten_1[0][0]']
dense_3 (Dense) (None, 128) 7680 ['concatenate_1[0][0]']
dropout_2 (Dropout) (None, 128) 0 ['dense_3[0][0]']
dense_4 (Dense) (None, 64) 8256 ['dropout_2[0][0]']
dropout_3 (Dropout) (None, 64) 0 ['dense_4[0][0]']
dense_5 (Dense) (None, 1) 65 ['dropout_3[0][0]']
==================================================================================================
Total params: 16,073
Trainable params: 16,073
Non-trainable params: 0
__________________________________________________________________________________________________
Epoch 1/10
1/1 [==============================] - 1s 900ms/step - loss: 0.2550 - mae: 0.4372 - val_loss: 0.5248 - val_mae: 0.7244
Epoch 2/10
1/1 [==============================] - 0s 40ms/step - loss: 0.1208 - mae: 0.2758 - val_loss: 0.4171 - val_mae: 0.6459
Epoch 3/10
1/1 [==============================] - 0s 33ms/step - loss: 0.0755 - mae: 0.1964 - val_loss: 0.3327 - val_mae: 0.5768
Epoch 4/10
1/1 [==============================] - 0s 39ms/step - loss: 0.2140 - mae: 0.4439 - val_loss: 0.2499 - val_mae: 0.4999
Epoch 5/10
1/1 [==============================] - 0s 35ms/step - loss: 0.0670 - mae: 0.2171 - val_loss: 0.1877 - val_mae: 0.4333
Epoch 6/10
1/1 [==============================] - 0s 29ms/step - loss: 0.6102 - mae: 0.7127 - val_loss: 0.1391 - val_mae: 0.3730
Epoch 7/10
1/1 [==============================] - 0s 31ms/step - loss: 0.2098 - mae: 0.4228 - val_loss: 0.1175 - val_mae: 0.3428
Epoch 8/10
1/1 [==============================] - 0s 36ms/step - loss: 0.1307 - mae: 0.3320 - val_loss: 0.1079 - val_mae: 0.3285
Epoch 9/10
1/1 [==============================] - 0s 30ms/step - loss: 0.3326 - mae: 0.5535 - val_loss: 0.1151 - val_mae: 0.3393
Epoch 10/10
1/1 [==============================] - 0s 29ms/step - loss: 0.0800 - mae: 0.2738 - val_loss: 0.1237 - val_mae: 0.3517
1/1 [==============================] - 0s 28ms/step - loss: 0.2376 - mae: 0.4874
Mean Absolute Error: 0.4874
- The above code set epoch number as 10, and the Mean Absolute Error: 0.4874
- After adjusting epoch from 10 to 100. The Mean Aboslute Error Mean Absolute Error becomes better and smaller: 0.3215