test_amazon-video_games5.yml

experiment:
  dataset:
    name: amazon-video_games-5
    item:
      path: datasets/amazon-video_games-5/processed/item.csv
      extra_features: [name, features, price, also_buy, also_view, brand, categories]
    user:
      path: datasets/amazon-video_games-5/processed/user.csv
      extra_features: [name]
    ratings:
      path: datasets/amazon-video_games-5/processed/rating.csv
      timestamp: False

  preprocess:
    # - method: filter_by_rating
    #   parameters:
    #     threshold: 20
    # - method: binarize
    #   parameters:
    #     threshold: 4
    - method: filter_kcore
      parameters:
        k: 5
        iterations: 1
        target: user # user or rating

  split:
    seed: 42
    # test:
    #   method: random_by_ratio
    #   level: global
    #   p: 0.2
    # validation:
    #   method: random_by_ratio
    #   level: global
    #   p: 0.2

    # test:
    #   method: timestamp_by_ratio
    #   level: user
    #   p: 0.1
    # validation:
    #   level: user
    #   method: timestamp_by_ratio
    #   p: 0.2

    # test:
    #   method: fixed_timestamp
    #   # type: global_level
    #   timestamp: 890000000
    # validation:
    #   method: fixed_timestamp
    #   timestamp: 880000000

    test:
      method: k_fold
      k: 2
      level: "user"

  models:
    - name: transE
      config:
        save_weights: True
      parameters:
        embedding_dim: 150
        scoring_fct_norm: 1
        epochs: 5
        seed: 42
        triples: ratings # only (ratings) or (all) triples for training

  evaluation:
    k: 5
    relevance_threshold: 0
    metrics: [MAP, nDCG]

  report:
    file: "experiment_results/amazon/run1_videogames5_transE.csv"