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Association Rule Mining
Association Rule Mining
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:
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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