Recommendation Systems

Recommendation systems are algorithms designed to suggest items or content to users based on their preferences, behaviors, or similarities with other users.

Association Rule Mining (ARM) is a technique used in recommendation systems to uncover patterns in transaction data. By analyzing user-item interactions, ARM identifies associations such as "users who buy X also tend to buy Y," enabling personalized recommendations.
For example, if a user has purchased item X, the system can recommend item Y based on this association.
This enhances the shopping experience by offering relevant suggestions based on individual preferences and behaviors.

Example

Let's discover a code example that demonstrates a simple recommendation system using Association Rule Mining:


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import pandas as pd
import numpy as np
from mlxtend.frequent_patterns import apriori, association_rules
from mlxtend.preprocessing import TransactionEncoder
import warnings

# Ignore warnings
warnings.filterwarnings('ignore')

# Generate synthetic transaction data
np.random.seed(42)
num_transactions = 10000  # Number of transactions to generate
min_items_per_transaction = 5  # Minimum number of items per transaction
max_items_per_transaction = 20  # Maximum number of items per transaction

# List of items available for transactions
items = ["bread", "milk", "eggs", "cheese", "butter", "yogurt", "apples", "bananas", "oranges", "grapes",
         "chicken", "beef", "pork", "fish", "rice", "pasta", "potatoes", "onions", "tomatoes", "lettuce"]

# Generate transactions
transactions = []
for _ in range(num_transactions):
    # Randomly select the number of items in each transaction
    num_items_in_transaction = np.random.randint(min_items_per_transaction, max_items_per_transaction + 1)
    # Randomly select items for the transaction
    transaction = list(np.random.choice(items, size=num_items_in_transaction, replace=True))
    transactions.append(transaction)

# Convert transaction data to DataFrame format
df = pd.DataFrame(transactions)

# Apply transaction encoding using TransactionEncoder
encoder = TransactionEncoder()
df_encoded = encoder.fit_transform(transactions)

# Convert encoded transactions to DataFrame format
df_encoded = pd.DataFrame(df_encoded, columns=encoder.columns_)

# Mine frequent itemsets using the Apriori algorithm
frequent_itemsets = apriori(df_encoded, min_support=0.05, use_colnames=True)

# Generate association rules based on frequent itemsets
rules = association_rules(frequent_itemsets, metric='confidence', min_threshold=0.5)

# Define items purchased by the user
user_items = {'beef', 'apples', 'bread'}

# Filter association rules to find relevant recommendations for the user
recommended_items = set()
for index, row in rules.iterrows():
    antecedent = set(row['antecedents'])
    consequent = set(row['consequents'])
    # Check if the user's items are a subset of the antecedent of the rule
    if antecedent.issuperset(user_items):
        # Add recommended items to the set, excluding items already purchased by the user
        recommended_items.update(consequent.difference(user_items))

# Print recommended items for the user
print('Recommended items for the user:', recommended_items)

As a result, we can conclude that if a person has purchased all these items: {'beef', 'apples', 'bread'}, we can recommend them to purchase all the goods provided in the result of the code sample execution.

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Section 3. Chapter 2