Models CollaborativeFilteringModel

---

class CollaborativeFilteringModel

Entity CollaborativeFilteringModel

Attributes

last_read_date	Read-only property - Last time this model’s data was accessed.
name	Set or get the name of the model object.
status	Read-only property - Current model life cycle status.

Methods

__init__(self[, name, _info])	Create a new Collaborative Filtering (ALS) model.
predict(self, frame, input_source_column_name, input_dest_column_name[, ...])	[BETA] Collaborative Filtering Predict (ALS).
recommend(self, entity_id[, number_of_recommendations, recommend_products])	[BETA] Collaborative Filtering Predict (ALS).
score(self, user_id, item_id)	[BETA] Collaborative Filtering Predict (ALS).
train(self, frame, source_column_name, dest_column_name, weight_column_name)	Collaborative filtering (ALS) model

__init__(self, name=None)

Create a new Collaborative Filtering (ALS) model.

Parameters:

name : unicode (default=None)

User supplied name.

Returns:

: Model

For details about Collaborative Filter (ALS) modelling, see Collaborative Filter.

Examples

>>> model = ta.CollaborativeFilteringModel()
[===Job Progress===]
>>> model.train(edge_frame, 'source', 'dest', 'weight')
[===Job Progress===]
>>> model.score(1,5)
[===Job Progress===]
>>> recommendations = model.recommend(1, 3, True)
[===Job Progress===]
>>> recommendations
[{u'rating': 0.04854799984010311, u'product': 4, u'user': 1}, {u'rating': 0.04045666535703035, u'product': 3, u'user': 1}, {u'rating': 0.030060528471388848, u'product': 5, u'user': 1}]
>>> recommendations = model.recommend(5, 2, False)
[===Job Progress===]