-animerg- Naruto -2002- Complete Series Movie... (2026)

import numpy as np from gensim.models import Word2Vec from sklearn.feature_extraction.text import TfidfVectorizer

# Combine Features textual_feature = get_textual_features(topic) metadata_feature = get_metadata_features() -AnimeRG- Naruto -2002- Complete Series Movie...

# Sample data topic = "-AnimeRG- Naruto -2002- Complete Series Movie..." import numpy as np from gensim

# Metadata Features def get_metadata_features(): genres = ["Action", "Adventure", "Fantasy"] # Example genres genre_vector = [1 if g in genres else 0 for g in ["Action", "Adventure", "Fantasy", "Comedy"]] # Assuming a fixed set of genres release_year = 2002 complete_series = 1 # Binary feature return np.array([release_year, complete_series] + genre_vector) complete_series] + genre_vector)