if __name__ == "__main__": app.run(debug=True) This example demonstrates a basic recommendation system using the NearestNeighbors algorithm from scikit-learn. You can extend and improve this feature by incorporating more advanced machine learning techniques and integrating it with your video platform.
# Sample video data videos = [ {"id": 1, "title": "Video 1", "resolution": "720p"}, {"id": 2, "title": "Video 2", "resolution": "1080p"}, {"id": 3, "title": "Video 3", "resolution": "720p"} ]
@app.route("/recommend", methods=["GET"]) def recommend(): user_id = request.args.get("user_id") user = next((u for u in users if u["id"] == user_id), None) if user: viewing_history = user["viewing_history"] # Use the recommendation system to suggest videos distances, indices = nn.fit_transform(viewing_history) recommended_videos = [videos[i] for i in indices[0]] return jsonify(recommended_videos) return jsonify([]) BigTitsRoundAsses 25 01 18 Red Eviee XXX 720p M...
# AI-powered recommendation system nn = NearestNeighbors(n_neighbors=3)
app = Flask(__name__)
# Sample user data users = [ {"id": 1, "name": "User 1", "viewing_history": [1, 2]}, {"id": 2, "name": "User 2", "viewing_history": [3]} ]
"Enhanced Video Discovery"
This feature aims to improve the user experience by providing a more efficient and personalized way to discover videos.
from flask import Flask, request, jsonify from sklearn.neighbors import NearestNeighbors if __name__ == "__main__": app
Here's a simple example using Python and the Flask web framework to give you an idea of how the feature could be implemented: