Recommendation Systems

Suggest items users will love.

What are Recommendation Systems?

Suggest products, movies, or content users might like.

Examples: Netflix shows, Amazon products, Spotify songs

Types of Recommendations

1. Content-Based: Based on item features
"You liked Action movies, here are more Action movies"

2. Collaborative Filtering: Based on user behavior
"Users like you also liked these movies"

3. Hybrid: Combine both approaches

Content-Based Filtering

from sklearn.metrics.pairwise import cosine_similarity
import pandas as pd

# Movie features: [action, comedy, drama, romance]
movies = pd.DataFrame({
    'title': ['Movie A', 'Movie B', 'Movie C'],
    'action': [1, 0, 1],
    'comedy': [0, 1, 0],
    'drama': [0, 1, 1]
})

# User watched Movie A
user_liked = movies[movies['title'] == 'Movie A'].iloc[0, 1:]

# Find similar movies
similarities = cosine_similarity(
    [user_liked], 
    movies.iloc[:, 1:]
)[0]

# Recommend top 2 (excluding Movie A itself)
recommendations = movies.iloc[similarities.argsort()[-3:-1]]
print(recommendations['title'])

Collaborative Filtering - User-Based

from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

# User-item ratings matrix
ratings = np.array([
    [5, 3, 0, 1],  # User 1
    [4, 0, 0, 1],  # User 2
    [1, 1, 0, 5],  # User 3
    [1, 0, 0, 4],  # User 4
])

# Find similar users to User 1
user_similarity = cosine_similarity(ratings)

# Get User 1's similar users
similar_users = user_similarity[0]

# Predict ratings for unwatched items
# (Weighted average of similar users' ratings)

Matrix Factorization

Advanced collaborative filtering:

from scipy.sparse.linalg import svds

# Decompose ratings matrix
U, sigma, Vt = svds(ratings, k=2)

# Reconstruct with predictions
predicted_ratings = np.dot(np.dot(U, np.diag(sigma)), Vt)

# Recommend highest predicted ratings

Using Surprise Library

from surprise import SVD, Dataset, Reader
from surprise.model_selection import cross_validate

# Load data
reader = Reader(rating_scale=(1, 5))
data = Dataset.load_from_df(df[['user_id', 'item_id', 'rating']], reader)

# Train SVD
model = SVD()
cross_validate(model, data, measures=['RMSE', 'MAE'], cv=5)

# Get prediction
prediction = model.predict(user_id='Tom', item_id='Movie123')
print(f"Predicted rating: {prediction.est}")

Deep Learning Approach

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Embedding, Flatten, Dense, Concatenate

# User and item inputs
user_input = Input(shape=(1,))
item_input = Input(shape=(1,))

# Embeddings
user_embedding = Embedding(n_users, 50)(user_input)
item_embedding = Embedding(n_items, 50)(item_input)

user_vec = Flatten()(user_embedding)
item_vec = Flatten()(item_embedding)

# Combine
concat = Concatenate()([user_vec, item_vec])
dense = Dense(128, activation='relu')(concat)
output = Dense(1)(dense)

model = Model([user_input, item_input], output)
model.compile(optimizer='adam', loss='mse')

Cold Start Problem

New User: No history to base recommendations on
Solution: Ask preferences, use popular items

New Item: No ratings yet
Solution: Use content features, show to diverse users

Evaluation

from sklearn.metrics import mean_absolute_error

# Predict ratings
predictions = model.predict(X_test)

# Calculate error
mae = mean_absolute_error(y_test, predictions)
print(f"MAE: {mae}")

Remember

• Collaborative filtering often best
• Handle cold start problem
• A/B test recommendations
• Balance accuracy with diversity