# Define a function to filter content def filter_content(search_query, content_list): # Create a TF-IDF vectorizer vectorizer = TfidfVectorizer() # Fit the vectorizer to the content list and transform the search query content_vectors = vectorizer.fit_transform(content_list) search_vector = vectorizer.transform([search_query]) # Calculate the cosine similarity between the search query and content vectors similarities = cosine_similarity(search_vector, content_vectors).flatten() # Return the top-N similar content top_n = 5 indices = np.argsort(-similarities)[:top_n] return [content_list[i] for i in indices] Oppai Odyssey | Guide Free
# Example usage: search_query = "private maya rose vanessa hillz swingers o new" content_list = [...] filtered_content = filter_content(search_query, content_list) print(filtered_content) Note that this is a simplified example, and you may need to add more functionality, such as handling user authentication, caching, and error handling, depending on your specific use case. Microsoft Toolkit 25 1 Download Free File
Content Filtering and Recommendation
import numpy as np from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity
Develop a feature that allows users to discover new content, such as articles, videos, or products, based on their search queries or interests.
This is a high-level example, and the actual implementation would depend on the technology stack and requirements.