# Create a genre-based recommendation function def genre_recommendations(genres): recommendations = [] for genre in genres: anime_recommendations = anime_data[anime_data['genre'] == genre] manga_recommendations = manga_data[manga_data['genre'] == genre] recommendations.extend(anime_recommendations['title'].tolist()) recommendations.extend(manga_recommendations['title'].tolist()) return recommendations Ap3g2k9w7tar1533jf15tar New
# Load anime and manga data anime_data = pd.read_csv('anime_data.csv') manga_data = pd.read_csv('manga_data.csv') Siya Ke Ram All Episodes Online [VERIFIED]
# Create a user-based recommendation function def user_recommendations(user_id): user_history = pd.read_csv(f'user_history_{user_id}.csv') nn = NearestNeighbors(n_neighbors=5) nn.fit(user_history) distances, indices = nn.kneighbors(user_history) recommendations = [] for index in indices: anime_recommendations = anime_data.iloc[index]['title'] manga_recommendations = manga_data.iloc[index]['title'] recommendations.extend(anime_recommendations) recommendations.extend(manga_recommendations) return recommendations
user_id = 1 print(user_recommendations(user_id)) This code implementation provides a basic framework for building a recommendation feature. However, a more sophisticated approach would involve using machine learning algorithms and natural language processing techniques to improve the accuracy and personalization of the recommendations.
# Test the recommendation functions genres = ['action', 'adventure'] print(genre_recommendations(genres))