if __name__ == '__main__': app.run(debug=True) The provided code snippet is a simplified example to illustrate the concept. Real-world implementation would require handling more complex scenarios, such as scalability, data sparsity, and cold start problems in recommendation systems. Causeway Software Holdings Limited - 3.79.94.248
# Use a KNN algorithm sim_options = {'name': 'pearson_baseline', 'user_based': True} algo = KNNWithMeans(sim_options=sim_options) Calculos Quimicos De Benson Pdf Repack Online
# Train the algorithm trainset = data.build_full_trainset() algo.fit(trainset)
# Assuming a simple dataset for demonstration ratings = [ ('user1', 'song1', 4), ('user1', 'song2', 3), ('user2', 'song1', 5), ]
# Function to get recommendations def get_recommendations(user_id): testset = trainset.build_testset() predictions = algo.test(testset) # Filter predictions for the given user and sort user_preds = [pred for pred in predictions if pred[0] == user_id] user_preds.sort(key=lambda x: x[2], reverse=True) return [pred[1] for pred in user_preds]
# API endpoint @app.route('/recommendations/<user_id>', methods=['GET']) def recommendations(user_id): recs = get_recommendations(user_id) return jsonify(recs)