RecommenderBase

class implicit.recommender_base.RecommenderBase

Defines a common interface for all recommendation models

abstract fit(user_items, show_progress=True, callback=None)

Trains the model on a sparse matrix of user/item/weight

Parameters

user_items (csr_matrix) – A sparse CSR matrix of shape (number_of_users, number_of_items). The nonzero entries in this matrix are the items that are liked by each user. The values are how confident you are that the item is liked by the user.
show_progress (bool, optional) – Whether to show a progress bar during fitting
callback (Callable, optional) – Callable function on each epoch with such arguments as epoch, elapsed time and progress

abstract recommend(userid, user_items, N=10, filter_already_liked_items=True, filter_items=None, recalculate_user=False, items=None)

Recommends items for users.

This method allows you to calculate the top N recommendations for a user or batch of users. Passing an array of userids instead of a single userid will tend to be more efficient, and allows multi-thread processing on the CPU.

This method has options for filtering out items from the results. You can both filter out items that have already been liked by the user with the filter_already_liked_items parameter, as well as pass in filter_items to filter out other items for all users in the batch. By default all items in the training dataset are scored, but by setting the ‘items’ parameter you can restrict down to a subset.

Example usage:

# calculate the top recommendations for a single user
ids, scores = model.recommend(0, user_items[0])

# calculate the top recommendations for a batch of users
userids = np.arange(10)
ids, scores = model.recommend(userids, user_items[userids])

Parameters

userid (Union[int, array_like]) – The userid or array of userids to calculate recommendations for
user_items (csr_matrix) – A sparse matrix of shape (users, number_items). This lets us look up the liked items and their weights for the user. This is used to filter out items that have already been liked from the output, and to also potentially recalculate the user representation. Each row in this sparse matrix corresponds to a row in the userid parameter: that is the first row in this matrix contains the liked items for the first user in the userid array.
N (int, optional) – The number of results to return
filter_already_liked_items (bool, optional) – When true, don’t return items present in the training set that were rated by the specified user.
filter_items (array_like, optional) – List of extra item ids to filter out from the output
recalculate_user (bool, optional) – When true, don’t rely on stored user embeddings and instead recalculate from the passed in user_items. This option isn’t supported by all models.
items (array_like, optional) – Array of extra item ids. When set this will only rank the items in this array instead of ranking every item the model was fit for. This parameter cannot be used with filter_items

Returns

Tuple of (itemids, scores) arrays. When calculating for a single user these array will be 1-dimensional with N items. When passed an array of userids, these will be 2-dimensional arrays with a row for each user.

Return type

tuple

abstract similar_users(userid, N=10, filter_users=None, users=None)

Calculates the most similar users for a userid or array of userids

Parameters

userid (Union[int, array_like]) – The userid or an array of userids to retrieve similar users for.
N (int, optional) – The number of similar users to return
filter_users (array_like, optional) – An array of user ids to filter out from the results being returned
users (array_like, optional) – An array of user ids to include in the output. If not set all users in the training set will be included. Cannot be used with the filter_users options

Returns

Tuple of (userids, scores) arrays

Return type

tuple

abstract similar_items(itemid, N=10, recalculate_item=False, item_users=None, filter_items=None, items=None)

Calculates a list of similar items

Parameters

itemid (Union[int, array_like]) – The item id or an array of item ids to retrieve similar items for
N (int, optional) – The number of similar items to return
recalculate_item (bool, optional) – When true, don’t rely on stored item state and instead recalculate from the passed in item_users
item_users (csr_matrix, optional) – A sparse matrix of shape (itemid, number_users). This lets us look up the users for each item. This is only needs to be set when setting recalculate_item=True. This should have the same number of rows as the itemid parameter, with the first row of the sparse matrix corresponding to the first item in the itemid array.
filter_items (array_like, optional) – An array of item ids to filter out from the results being returned
items (array_like, optional) – An array of item ids to include in the output. If not set all items in the training set will be included. Cannot be used with the filter_items options

Returns

Tuple of (itemids, scores) arrays

Return type

tuple

abstract save(file)

Saves the model to a file, using the numpy .npz format

Parameters: file (str or io.IOBase) – Either the filename or an open file-like object to save the model to