Recommender Model
Distributed datasets containing information such as customer ratings or product purchases can be used to build a recommender model. This model can then be used to suggest items to customers with similar histories. Sensitive information such as individual purchase history can remain in place with the owner, but the insights of purchase trends can be extracted while still retaining privacy. This operation supports Federated Inference.
Operation
- When using
add_agreement()
to forge an agreement on a trained Recommender Model, useOperation.EXECUTE
for theoperation
parameter. - When using
add_agreement()
to allow a counterparty to use your dataset for model training, or when usingcreate_job()
to train a Recommender Model, useOperation.RECOMMENDER_TRAIN
for theoperation
parameter.
Parameters
Training parameters
user_id_column: str
item_id_column: str
rating_column: str
epochs: int = 5
learning_rate: float = 1e-3
hidden_dim: int = 100
reg_u: int = 1e-4
reg_v: int = 1e-4
ℹ️Recommendation model will train models locally per data provider then adjust gradients at the initiating party.
Inference parameters
user_id_column: str = ""
top_n: int = 10