What is Content-Based Filtering?

Content-Based Filtering is a recommendation technique that suggests items to users based on the features of items they liked in the past. It focuses on the content of items — not on the behavior of other users.

Real-Life Example

• If you watched a Marvel action movie, the system recommends more action superhero films.
• If you liked a Bollywood song, similar songs by the same artist or genre will appear.
• If you read a technology article, the system shows similar tech blogs.

Why Content-Based Filtering is Important?

• Works well for new users.
• Provides personalized suggestions.
• Does not depend on other users' data.
• Best for niche & specific interests.
• Used in news, music, movies, articles, e-commerce.

How Content-Based Filtering Works

Key Components

Examples of Item Features

Common Similarity Measures

Cosine Similarity Formula

Where:

• A & B = item vectors
• Cosine value ranges from 0 to 1
• Higher score = more similar items

Python Example — Content-Based Filtering

pip install scikit-learn pandas

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# Sample movies
data = {
    "title": ["Iron Man", "Avengers", "Titanic", "Notebook", "Spider-Man"],
    "description": [
        "Action superhero technology suit",
        "Action superhero team save world",
        "Romance love sea ship",
        "Romance love story emotional",
        "Action superhero spider hero"
    ]
}

df = pd.DataFrame(data)

# Convert text to vectors
vectorizer = TfidfVectorizer()
tfidf = vectorizer.fit_transform(df["description"])

# Calculate similarity
similarity = cosine_similarity(tfidf)

# Recommendation function
def recommend(movie):
    idx = df[df["title"] == movie].index[0]
    scores = list(enumerate(similarity[idx]))
    scores = sorted(scores, key=lambda x: x[1], reverse=True)[1:4]
    return [df["title"][i[0]] for i in scores]

print(recommend("Iron Man"))

Real-Life Analogy

Workflow Visual

Real-World Applications

Advantages

• Works for new users.
• Highly personalized.
• Doesn't require other users' data.
• Easy to implement.
• Works for niche interests.

Disadvantages

Common Mistakes to Avoid

Best Practices

Importance of Content-Based Filtering

Golden Rule