آموزش ساخت سیستم پیشنهادگر با پایتون

pip install pandas scikit-learn

import pandas as pd

# داده‌های نمونه
data = {
    'title': ['The Shawshank Redemption', 'The Godfather', 'The Dark Knight', 'Pulp Fiction', 'Forrest Gump'],
    'description': [
        'Two imprisoned men bond over a number of years, finding solace and eventual redemption through acts of common decency.',
        'The aging patriarch of an organized crime dynasty transfers control of his clandestine empire to his reluctant son.',
        'When the menace known as the Joker emerges from his mysterious past, he wreaks havoc and chaos on the people of Gotham.',
        'The lives of two mob hitmen, a boxer, a gangster’s wife, and a pair of diner bandits intertwine in four tales of violence and redemption.',
        'The presidencies of Kennedy and Johnson, the Vietnam War, the counterculture, and other historical events unfold through the perspective of an Alabama man.'
    ]
}

df = pd.DataFrame(data)

from sklearn.feature_extraction.text import TfidfVectorizer

# ایجاد تبدیل‌کننده TF-IDF
tfidf = TfidfVectorizer(stop_words='english')
tfidf_matrix = tfidf.fit_transform(df['description'])

print(tfidf_matrix.shape)  # نمایش ابعاد ماتریس

from sklearn.metrics.pairwise import cosine_similarity

# محاسبه شباهت
cosine_sim = cosine_similarity(tfidf_matrix, tfidf_matrix)

# نمایش نتایج شباهت
print(cosine_sim)

def get_recommendations(title):
    # یافتن ایندکس فیلم
    idx = df[df['title'] == title].index[]

    # دریافت امتیازات شباهت‌ها
    sim_scores = list(enumerate(cosine_sim[idx]))

    # مرتب‌سازی فیلم‌ها بر اساس شباهت
    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)

    # انتخاب 10 فیلم مشابه
    sim_scores = sim_scores[1:6]

    # دریافت ایندکس‌های فیلم‌های مشابه
    movie_indices = [i[] for i in sim_scores]

    # برگرداندن فیلم‌های پیشنهادی
    return df['title'].iloc[movie_indices]

# تست تابع
recommended_movies = get_recommendations('The Shawshank Redemption')
print(recommended_movies)