ALS#

class libreco.algorithms.ALS(task, data_info, embed_size=16, n_epochs=10, reg=None, alpha=10, use_cg=True, n_threads=1, seed=42, lower_upper_bound=None)[source]#

Bases: EmbedBase

Alternating Least Squares algorithm.

One can use conjugate gradient optimization and set more n_threads to accelerate training.

Parameters:

  • task ({'rating', 'ranking'}) – Recommendation task. See Task.

  • data_info (DataInfo object) – Object that contains useful information for training and inference.

  • embed_size (int, default: 16) – Vector size of embeddings.

  • n_epochs (int, default: 10) – Number of epochs for training.

  • reg (float or None, default: None) – Regularization parameter, must be non-negative or None.

  • alpha (int, default: 10) – Parameter used for increasing confidence level, only applied for ranking task.

  • use_cg (bool, default: True) – Whether to use conjugate gradient optimization. See reference.

  • n_threads (int, default: 1) – Number of threads to use.

  • seed (int, default: 42) – Random seed.

  • lower_upper_bound (tuple or None, default: None) – Lower and upper score bound for rating task.

References

[1] Haoming Li et al. Matrix Completion via Alternating Least Square(ALS).

[2] Yifan Hu et al. Collaborative Filtering for Implicit Feedback Datasets.

[3] Gábor Takács et al. Applications of the Conjugate Gradient Method for Implicit Feedback Collaborative Filtering.

fit(train_data, neg_sampling, verbose=1, shuffle=True, eval_data=None, metrics=None, k=10, eval_batch_size=8192, eval_user_num=None, **kwargs)[source]#

Fit ALS model on the training data.

Parameters:

  • train_data (TransformedSet object) – Data object used for training.

  • neg_sampling (bool) –

    Whether to perform negative sampling for evaluating data.

    New in version 1.1.0.

  • verbose (int, default: 1) – Print verbosity. If eval_data is provided, setting it to higher than 1 will print evaluation metrics during training.

  • shuffle (bool, default: True) – Whether to shuffle the training data.

  • eval_data (TransformedSet object, default: None) – Data object used for evaluating.

  • metrics (list or None, default: None) – List of metrics for evaluating.

  • k (int, default: 10) – Parameter of metrics, e.g. recall at k, ndcg at k

  • eval_batch_size (int, default: 8192) – Batch size for evaluating.

  • eval_user_num (int or None, default: None) – Number of users for evaluating. Setting it to a positive number will sample users randomly from eval data.

save(path, model_name, **kwargs)[source]#

Save model for inference or retraining.

Parameters:

  • path (str) – File folder path to save model.

  • model_name (str) – Name of the saved model file.

rebuild_model(path, model_name)[source]#

Reconstruct model for retraining.

Parameters:

  • path (str) – File folder path for saved model.

  • model_name (str) – Name of the saved model file.

get_item_embedding(item=None, include_bias=False)#

Get item embedding(s) from the model.

Parameters:

  • item (int or str or None, default: None) – Query item id. If it is None, all item embeddings will be returned.

  • include_bias (bool, default: False) – Whether to include bias term in returned embeddings.

Returns:

item_embedding – Returned item embeddings.

Return type:

numpy.ndarray

Raises:

  • ValueError – If the item does not appear in the training data.

  • AssertionError – If the model has not been trained.

get_user_embedding(user=None, include_bias=False)#

Get user embedding(s) from the model.

Parameters:

  • user (int or str or None, default: None) – Query user id. If it is None, all user embeddings will be returned.

  • include_bias (bool, default: False) – Whether to include bias term in returned embeddings.

Returns:

user_embedding – Returned user embeddings.

Return type:

numpy.ndarray

Raises:

  • ValueError – If the user does not appear in the training data.

  • AssertionError – If the model has not been trained.

init_knn(approximate, sim_type, M=100, ef_construction=200, ef_search=200)#

Initialize k-nearest-search model.

Parameters:

  • approximate (bool) – Whether to use approximate nearest neighbor search. If it is True, nmslib must be installed. The HNSW method in nmslib is used.

  • sim_type ({'cosine', 'inner-product'}) – Similarity space type.

  • M (int, default: 100) – Parameter in HNSW, refer to nmslib doc.

  • ef_construction (int, default: 200) –

    Parameter in HNSW, refer to nmslib doc.

  • ef_search (int, default: 200) –

    Parameter in HNSW, refer to nmslib doc.

Raises:

  • ValueError – If sim_type is not one of (‘cosine’, ‘inner-product’).

  • ModuleNotFoundError – If approximate=True and nmslib is not installed.

classmethod load(path, model_name, data_info, **kwargs)#

Load saved embed model for inference.

Parameters:

  • path (str) – File folder path to save model.

  • model_name (str) – Name of the saved model file.

  • data_info (DataInfo object) – Object that contains some useful information.

Returns:

model – Loaded embed model.

Return type:

type(cls)

See also

save

predict(user, item, cold_start='average', inner_id=False)#

Make prediction(s) on given user(s) and item(s).

Parameters:

  • user (int or str or array_like) – User id or batch of user ids.

  • item (int or str or array_like) – Item id or batch of item ids.

  • cold_start ({'popular', 'average'}, default: 'average') –

    Cold start strategy.

    • ’popular’ will sample from popular items.

    • ’average’ will use the average of all the user/item embeddings as the representation of the cold-start user/item.

  • inner_id (bool, default: False) – Whether to use inner_id defined in libreco. For library users inner_id may never be used.

Returns:

prediction – Predicted scores for each user-item pair.

Return type:

float or numpy.ndarray

recommend_user(user, n_rec, cold_start='average', inner_id=False, filter_consumed=True, random_rec=False)#

Recommend a list of items for given user(s).

Parameters:

  • user (int or str or array_like) – User id or batch of user ids to recommend.

  • n_rec (int) – Number of recommendations to return.

  • cold_start ({'popular', 'average'}, default: 'average') –

    Cold start strategy.

    • ’popular’ will sample from popular items.

    • ’average’ will use the average of all the user/item embeddings as the representation of the cold-start user/item.

  • inner_id (bool, default: False) – Whether to use inner_id defined in libreco. For library users inner_id may never be used.

  • filter_consumed (bool, default: True) – Whether to filter out items that a user has previously consumed.

  • random_rec (bool, default: False) – Whether to choose items for recommendation based on their prediction scores.

Returns:

recommendation – Recommendation result with user ids as keys and array_like recommended items as values.

Return type:

dict of {Union[int, str, array_like] : numpy.ndarray}

search_knn_items(item, k)#

Search most similar k items.

Parameters:

  • item (int or str) – Query item id.

  • k (int) – Number of similar items.

Returns:

similar items – A list of k similar items.

Return type:

list

search_knn_users(user, k)#

Search most similar k users.

Parameters:

  • user (int or str) – Query user id.

  • k (int) – Number of similar users.

Returns:

similar users – A list of k similar users.

Return type:

list