run_ml-100k.yml

experiment:
  dataset:
    name: ml-100k
    item: # infos related to item dataset (mandatory, at least item_id)
      path: datasets/ml-100k/processed/item.csv
      extra_features: [movie_year, movie_title] # features(columns) beside item_id to be used
    user: # mandatory (at least user_id)
      path: datasets/ml-100k/processed/user.csv
      extra_features: [gender, occupation] # features beside user_id
    ratings: # mandatory (at least [user_id, item_id, rating])
      path: datasets/ml-100k/processed/rating.csv
      timestamp: True

  preprocess:
    # - method: filter_by_rating
    #   parameters:
    #     threshold: 20
    # - method: binarize
    #   parameters:
    #     threshold: 4
    - method: filter_kcore
      parameters:
        k: 20
        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: 5
      level: "user"

  models:
    - name: deepwalk_based
      config:
        save_weights: True
      parameters:
        walk_len: 10
        p: 1.0
        q: 1.0
        n_walks: 50
        embedding_size: 64
        epochs: 1
    - name: deepwalk_based
      config:
      parameters:
        walk_len: 10
        p: 0.8
        q: 0.6
        n_walks: 50
        embedding_size: 64
        epochs: 1
    - name: transE
      config:
        save_weights: True
      parameters:
        embedding_dim: 150
        scoring_fct_norm: 1
        epochs: 25
        seed: 42
        triples: ratings # only (ratings) or (all) triples for training
    - name: transH
      config:
        save_weights: True
      parameters:
        embedding_dim: 150
        scoring_fct_norm: 2
        epochs: 25
        seed: 42
        triples: ratings # only (ratings) or (all) triples for training
    - name: transR
      config:
        save_weights: True
      parameters:
        embedding_dim: 150
        relation_dim: 90
        scoring_fct_norm: 2
        epochs: 25
        seed: 42
        triples: all # only (ratings) or (all) triples for training
    - name: transD
      config:
        save_weights: True
      parameters:
        embedding_dim: 150
        epochs: 25
        seed: 42
        triples: ratings # only (ratings) or (all) triples for training
    - name: tuckER
      config:
        save_weights: True
      parameters:
        embedding_dim: 200
        epochs: 25
        seed: 42
        triples: ratings # only (ratings) or (all) triples for training
    - name: rESCAL
      config:
        save_weights: True
      parameters:
        embedding_dim: 50
        epochs: 25
        seed: 42
        triples: ratings # only (ratings) or (all) triples for training
    - name: distMult
      config:
        save_weights: True
      parameters:
        embedding_dim: 50
        epochs: 25
        seed: 42
        triples: all # only (ratings) or (all) triples for training
    - name: complEx
      config:
        save_weights: True
      parameters:
        embedding_dim: 100
        epochs: 25
        seed: 42
        triples: ratings # only (ratings) or (all) triples for training
    - name: rotatE
      config:
        save_weights: True
      parameters:
        embedding_dim: 200
        epochs: 25
        seed: 42
        triples: all # only (ratings) or (all) triples for training
    - name: ePHEN
      config:
        save_weights: True
      parameters:
        embedding_model: sentence-transformers/all-roberta-large-v1
        embed_with: movie_title
        iterations: 30
        mi: 0.5
    - name: ePHEN
      config:
        save_weights: True
      parameters:
        embedding_model: deepwalk_based
        embedding_model_kwargs:
          config:
            save_weights: True
          parameters:
            walk_len: 10
            p: 1.0
            q: 1.0
            n_walks: 50
            embedding_size: 64
            epochs: 1
        embed_with: graph
        iterations: 30
        mi: 0.5

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

  report:
    file: "experiment_results/ml-100k.csv"
    execution_times:
      file: "experiment_results/ml-100k_times.csv"