PopularModel

class rectools.models.popular.PopularModel(popularity: str = 'n_users', period: Optional[timedelta] = None, begin_from: Optional[datetime] = None, add_cold: bool = False, inverse: bool = False, verbose: int = 0)[source]

Bases: FixedColdRecoModelMixin, ModelBase

Model generating recommendations based on popularity of items.

Parameters

  • popularity ({“n_users”, “n_interactions”, “mean_weight”, “sum_weight”}, default “n_users”) – Method of calculating item popularity. To evaluate popularity score the following methods are available: - n_users - number of unique users that interacted with item; - n_interactions - number of interactions with item; - mean_weight - mean item interactions weight; - sum_weight - total item interactions weight.

  • period (timedelta, optional, default None) – Period before last interaction to consider interactions for popularity calculation. Either period or begin_from can be set at once. If both are None all interactions will be used.

  • begin_from (datetime, optional, default None) – Exact datetime to consider interactions from for popularity calculation. Either period or begin_from can be set at once. If both are None all interactions will be used.

  • add_cold (bool, default False) – If True cold items will be added to the end of popularity list and can be recommended. Item is cold if it’s not present in interactions at all (but present in id map) or not present in last interactions defined by either period or begin_from arguments. Order of cold items is unpredictable. Cold items score will be equal to 0.

  • inverse (bool, default False) – If True least popular items will be selected.

  • verbose (int, default 0) – Degree of verbose output. If 0, no output will be provided.

Inherited-members

Methods

fit(dataset, *args, **kwargs)

Fit model.

recommend(users, dataset, k, filter_viewed)

Recommend items for users.

recommend_to_items(target_items, dataset, k)

Recommend items for target items.

Attributes

recommends_for_cold

recommends_for_warm