experiment:
  dataset: movielens_small
  data_config:
    strategy: dataset
    dataset_path: ../data/{0}/dataset.tsv
    side_information:
      - dataloader: ItemAttributes
        attribute_file: ../data/{0}/item_features.tsv
  splitting:
    save_on_disk: True
    save_folder: ../data/{0}/splitting/
    test_splitting:
      strategy: temporal_hold_out
      test_ratio: 0.2
  top_k: 10
  evaluation:
    cutoffs: 10
    simple_metrics: [nDCG, Recall, HR, Precision, MAP, MRR]
  gpu: -1
  external_models_path: ../external/models/__init__.py
  models:
    Random:
      meta:
        save_recs: True
    MostPop:
      meta:
        save_recs: True
    AttributeItemKNN:
      meta:
        save_recs: True
        save_weights: True
        verbose: True
      neighbors: [15, 30, 60, 100]
      similarity: [cosine, correlation]
    VSM:
      meta:
        save_recs: True
        save_weights: True
        verbose: True
      similarity: [cosine, correlation]
      user_profile: [tfidf, binary]
      item_profile: [tfidf, binary]