Build a movie recommendation bot

Here is a simple movie recommendation bot built with Python. It uses content-based filtering (based on movie genres and descriptions) to recommend similar movies.

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

# Sample movie dataset (you can expand this or load from a CSV)
data = {
    'title': [
        'The Matrix', 'Inception', 'Interstellar', 'The Dark Knight', 
        'Fight Club', 'Pulp Fiction', 'Forrest Gump', 'The Shawshank Redemption'
    ],
    'description': [
        'A computer hacker learns about the true nature of reality and his role in the war against its controllers.',
        'A thief who steals corporate secrets through dream-sharing is given an inverse task of planting an idea.',
        'A team of explorers travel through a wormhole in space in an attempt to ensure humanity’s survival.',
        'Batman faces the Joker, a criminal mastermind who plunges Gotham into chaos.',
        'An office worker forms an underground fight club that evolves into something much more.',
        'The lives of two mob hitmen, a boxer, and others intertwine in a tale of violence and redemption.',
        'The life journey of Forrest Gump, a man with a low IQ who achieves incredible things.',
        'Two imprisoned men bond over a number of years, finding solace and eventual redemption.'
    ]
}

# Create a DataFrame
df = pd.DataFrame(data)

# Vectorize the descriptions using TF-IDF
tfidf = TfidfVectorizer(stop_words='english')
tfidf_matrix = tfidf.fit_transform(df['description'])

# Compute cosine similarity
cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)

# Function to get recommendations
def get_recommendations(title, cosine_sim=cosine_sim):
    # Get the index of the movie that matches the title
    idx = df.index[df['title'] == title].tolist()
    if not idx:
        return ["Movie not found."]
    idx = idx[0]

    # Get the pairwise similarity scores of all movies with that movie
    sim_scores = list(enumerate(cosine_sim[idx]))

    # Sort the movies based on similarity scores
    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)

    # Get the scores of the 5 most similar movies
    sim_scores = sim_scores[1:6]

    # Get the movie indices
    movie_indices = [i[0] for i in sim_scores]

    # Return the top 5 most similar movies
    return df['title'].iloc[movie_indices].tolist()

# Example usage
if __name__ == "__main__":
    user_input = input("Enter a movie you like: ")
    recommendations = get_recommendations(user_input)
    print("nYou might also like:")
    for movie in recommendations:
        print("- " + movie)

How It Works:

• Takes user input (a movie title).

• Finds similar movies based on TF-IDF of the movie description.

• Returns the top 5 most similar movies.

To Enhance This Bot:

• Use a larger dataset like MovieLens.

• Add genre-based or collaborative filtering logic.

• Build a web-based version with Flask or FastAPI.

• Include user ratings for better personalization.

Let me know if you’d like the bot in a different format (like a chatbot, web app, or Telegram bot).