Prefiltering Data

After data loading,Elliot provides data filtering operations through two possible strategies. The first strategy implemented in the Prefiltering module isFilter-by-rating, which drops off a user-item interaction if the preference score is smaller than a given threshold. It can be (i) a Numerical value, e.g.,3.5, (ii)a Distributional detail, e.g., global rating average value, or (iii) a user-based distributional (User Dist.) value, e.g., user’s average rat-ing value. The second prefiltering strategy,𝑘-core, filters out users,items, or both, with less than𝑘recorded interactions. The 𝑘-core strategy can proceed iteratively (Iterative𝑘-core) on both users and items until the 𝑘-core filtering condition is met, i.e., all the users and items have at least𝑘recorded interaction. Since reaching such condition might be intractable, Elliot allows specifying the maximum number of iterations (Iter-n-rounds). Finally, the Cold-Users filtering feature allows retaining cold-users only.

Moreover it is possible to specify multiple prefiltering steps by set multiple strategy into prefiltering section.

Elliot provides several prefiltering strategies. To enable Prefiltering operations, we can insert the corresponding block into our config file:

experiment:
  prefiltering:
    - strategy: global_threshold|user_average|user_k_core|item_k_core|iterative_k_core|n_rounds_k_core|cold_users
      threshold: 3|average
      core: 5
      rounds: 2
    - strategy: global_threshold|user_average|user_k_core|item_k_core|iterative_k_core|n_rounds_k_core|cold_users
      threshold: 3|average
      core: 5
      rounds: 2

In detail, Elliot provides eight main prefiltering approaches: global_threshold, user_average, user_k_core, item_k_core, iterative_k_core, n_rounds_k_core, cold_users.

global_threshold assumes a single system-wise threshold to filter out irrelevant transactions. global_threshold takes one mandatory parameter, threshold. threshold takes, as values, a float (ratings >= threshold will be kept), or the string average. With average, the system computes the global mean of the rating values and filters out all the ratings below.

experiment:
  prefiltering:
    strategy: global_threshold
    threshold: 3

experiment:
  prefiltering:
    strategy: global_threshold
    threshold: average

user_average has no parameters, and the system filters out the ratings below each user rating values mean.

experiment:
  prefiltering:
    strategy: user_average

user_k_core filters out all the users with a number of transactions lower than the given k core. It takes a parameter, core, where the user passes an int corresponding to the desired value.

experiment:
  prefiltering:
    strategy: user_k_core
    core: 5

item_k_core filters out all the items with a number of transactions lower than the given k core. It takes a parameter, core, where the user passes an int corresponding to the desired value.

experiment:
  prefiltering:
    strategy: item_k_core
    core: 5

iterative_k_core runs iteratively user_k_core, and item_k_core until the dataset is no further modified. It takes a parameter, core, where the user passes an int corresponding to the desired value.

experiment:
  prefiltering:
    strategy: iterative_k_core
    core: 5

n_rounds_k_core runs iteratively user_k_core, and item_k_core for a specified number of rounds. It takes the first parameter, core, where the user passes an int corresponding to the desired value. It takes the second parameter, rounds, where the user passes an int corresponding to the desired value.

experiment:
  prefiltering:
    strategy: n_rounds_k_core
    core: 5
    rounds: 2

cold_users filters out all the users with a number of interactions higher than a given threshold. It takes a parameter, threshold, where the user passes an int corresponding to the desired value.

experiment:
  prefiltering:
    strategy: cold_users
    threshold: 3