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# Sample user data users = [ {"id": 1, "name": "User 1", "viewing_history": [1, 2]}, {"id": 2, "name": "User 2", "viewing_history": [3]} ]

app = Flask(__name__)

@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...

Here's a simple example using Python and the Flask web framework to give you an idea of how the feature could be implemented:

# 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"} ] # Sample user data users = [ {"id":

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.

This feature aims to improve the user experience by providing a more efficient and personalized way to discover videos. jsonify from sklearn.neighbors import NearestNeighbors

"Enhanced Video Discovery"

# AI-powered recommendation system nn = NearestNeighbors(n_neighbors=3)

from flask import Flask, request, jsonify from sklearn.neighbors import NearestNeighbors