# Combine description and tags for analysis videos['combined'] = videos['description'] + ' ' + videos['tags']
# TF-IDF Vectorizer vectorizer = TfidfVectorizer() tfidf = vectorizer.fit_transform(videos['combined'])
# Compute similarities similarities = linear_kernel(tfidf, tfidf)
# Example usage print(recommend(0)) This example is highly simplified and intended to illustrate basic concepts. A real-world application would require more complexity, including handling larger datasets, more sophisticated algorithms, and integration with a robust backend and frontend. The development of a feature analyzing or recommending video content involves collecting and analyzing metadata, understanding user preferences, and implementing a recommendation algorithm. The example provided is a basic illustration and might need significant expansion based on specific requirements and the scale of the application.
# Sample video metadata videos = pd.DataFrame({ 'title': ['Video1', 'Video2', 'Video3'], 'description': ['This is video1 about MILFs', 'Video2 is about something else', 'Video3 is a hot video'], 'tags': ['MILFs, fun', 'comedy', 'hot, video'] })
import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import linear_kernel
Milfs Tres Demandeuses -hot Video- 2024 Web-dl ... -
# Combine description and tags for analysis videos['combined'] = videos['description'] + ' ' + videos['tags']
# TF-IDF Vectorizer vectorizer = TfidfVectorizer() tfidf = vectorizer.fit_transform(videos['combined']) MILFs Tres Demandeuses -Hot Video- 2024 WEB-DL ...
# Compute similarities similarities = linear_kernel(tfidf, tfidf) The example provided is a basic illustration and
# Example usage print(recommend(0)) This example is highly simplified and intended to illustrate basic concepts. A real-world application would require more complexity, including handling larger datasets, more sophisticated algorithms, and integration with a robust backend and frontend. The development of a feature analyzing or recommending video content involves collecting and analyzing metadata, understanding user preferences, and implementing a recommendation algorithm. The example provided is a basic illustration and might need significant expansion based on specific requirements and the scale of the application. 'description': ['This is video1 about MILFs'
# Sample video metadata videos = pd.DataFrame({ 'title': ['Video1', 'Video2', 'Video3'], 'description': ['This is video1 about MILFs', 'Video2 is about something else', 'Video3 is a hot video'], 'tags': ['MILFs, fun', 'comedy', 'hot, video'] })
import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import linear_kernel