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{
"name": "09_Recommendation_System_NCF_MovieLens_ML",
"query": "Help me develop a system to recommend movies based on user ratings from the MovieLens dataset using a Neural Collaborative Filtering (NCF) approach. First, load the dataset and split it into training and testing sets in `src/data_loader.py`. Next, implement the NCF approach and a matrix factorization baseline in `src/model.py`. Using these, print an example of the top 10 recommendations for a test user the NCF approach and the baseline and save them to `results/metrics/top_10_recommendations.txt`. It would be good if these sample recommendations were meaningful given the test user. Evaluate the system's performance using RMSE, MAE, etc., and save the results of this evaluation to `results/metrics/evaluation_metrics.txt`. Try and ensure that there is robust path handling that can deal with missing directories and such when saving files.",
"tags": [
"Recommender Systems",
"Supervised Learning"
],
"requirements": [
{
"requirement_id": 0,
"prerequisites": [],
"criteria": "The \"Neural Collaborative Filtering (NCF)\" algorithm is implemented in `src/model.py`.",
"category": "Machine Learning Method",
"satisfied": null
},
{
"requirement_id": 1,
"prerequisites": [],
"criteria": "The \"MovieLens\" dataset is loaded in 'src/data_loader.py'.",
"category": "Dataset or Environment",
"satisfied": null
},
{
"requirement_id": 2,
"prerequisites": [
1
],
"criteria": "Data is split into training and testing sets in `src/data_loader.py`.",
"category": "Data preprocessing and postprocessing",
"satisfied": null
},
{
"requirement_id": 3,
"prerequisites": [],
"criteria": "A matrix factorization baseline is implemented in in `src/model.py`.",
"category": "Machine Learning Method",
"satisfied": null
},
{
"requirement_id": 4,
"prerequisites": [
0,
1,
2,
3
],
"criteria": "The top 10 recommendations for a test user under the \"NCF\" approach and the baseline are saved in `results/metrics/top_10_recommendations.txt`.",
"category": "Other",
"satisfied": null
},
{
"requirement_id": 5,
"prerequisites": [
0,
1,
2,
3
],
"criteria": "The recommendation system performance is evaluated, including with \"RMSE\" and \"MAE,\" and the results are saved as `results/metrics/evaluation_metrics.txt`.",
"category": "Performance Metrics",
"satisfied": null
}
],
"preferences": [
{
"preference_id": 0,
"criteria": "Robust path handling is implemented to deal with things like missing directories.",
"satisfied": null
},
{
"preference_id": 1,
"criteria": "The top 10 recommendations should be clear and relevant to the sample user's preferences.",
"satisfied": null
}
],
"is_kaggle_api_needed": false,
"is_training_needed": true,
"is_web_navigation_needed": false
} |