content-based.py

from sklearn.metrics.pairwise import sigmoid_kernel

# Compute the sigmoid kernel
sig = sigmoid_kernel(tfv_matrix, tfv_matrix)

# Reverse mapping of indices and movie titles
indices = pd.Series(movies_clean.index, index=movies_clean['original_title']).drop_duplicates()

# Credit to Ibtesam Ahmed for the skeleton code
def give_rec(title, sig=sig):
    # Get the index corresponding to original_title
    idx = indices[title]

    # Get the pairwsie similarity scores 
    sig_scores = list(enumerate(sig[idx]))

    # Sort the movies 
    sig_scores = sorted(sig_scores, key=lambda x: x[1], reverse=True)

    # Scores of the 10 most similar movies
    sig_scores = sig_scores[1:11]

    # Movie indices
    movie_indices = [i[0] for i in sig_scores]

    # Top 10 most similar movies
    return movies_clean['original_title'].iloc[movie_indices]