graphlab don't recommend items already owned by user

Question

what should one specify when creating a graphlab recommender model such that the item that a user already own is not recommended to him again? Can this be done directly by specifying certain parameters or do I need to write a recommender from scratch.? data would look something like this

| user_id    |  item_id    | othercolumns |
|:-----------|------------:|:------------:|
| 1          |     21      |     This     | 
| 2          |     22      |     column   |
| 1          |     23      |     will     |
| 3          |     24      |     hold     |
| 2          |     25      |     other    |
| 1          |     26      |     values   |

Since item 21,23 and 26 are already owned by user 1 this item should not be recommended to him.

score 1 · Accepted Answer · answered Sep 04 '16 at 10:15

This behaviour is controlled by the exclude_known parameter of the recommender.recommend method (doc).

exclude_known : bool, optional

By default, all user-item interactions previously seen in the training data, or in any new data provided using new_observation_data.., are excluded from the recommendations. Passing in exclude_known = False overrides this behavior.

Example

>>> import graphlab as gl
>>> sf = gl.SFrame({'user_id':[1,2,1,3,2,1], 'item_id':[21,22,23,24,25,26]})
>>> print sf
+---------+---------+
| item_id | user_id |
+---------+---------+
|    21   |    1    |
|    22   |    2    |
|    23   |    1    |
|    24   |    3    |
|    25   |    2    |
|    26   |    1    |
+---------+---------+
[6 rows x 2 columns]
>>> rec_model = gl.recommender.create(sf)
>>> # we recommend items not owned by user
>>> rec_wo_own_item = rec_model.recommend(sf['user_id'].unique())
>>> rec_wo_own_item.sort('user_id').print_rows(100)
+---------+---------+----------------+------+
| user_id | item_id |     score      | rank |
+---------+---------+----------------+------+
|    1    |    22   |      0.0       |  1   |
|    1    |    24   |      0.0       |  2   |
|    1    |    25   |      0.0       |  3   |
|    2    |    21   |      0.0       |  1   |
|    2    |    23   |      0.0       |  2   |
|    2    |    24   |      0.0       |  3   |
|    2    |    26   |      0.0       |  4   |
|    3    |    21   | 0.333333333333 |  1   |
|    3    |    23   | 0.333333333333 |  2   |
|    3    |    26   | 0.333333333333 |  3   |
|    3    |    22   | 0.166666666667 |  4   |
|    3    |    25   | 0.166666666667 |  5   |
+---------+---------+----------------+------+
[12 rows x 4 columns]
>>> # we recommend items owned by user
>>> rec_w_own_item = rec_model.recommend(sf['user_id'].unique(), exclude_known=False)
>>> rec_w_own_item.sort('user_id').print_rows(100)
+---------+---------+----------------+------+
| user_id | item_id |     score      | rank |
+---------+---------+----------------+------+
|    1    |    21   | 0.666666666667 |  1   |
|    1    |    23   | 0.666666666667 |  2   |
|    1    |    26   | 0.666666666667 |  3   |
|    1    |    22   |      0.0       |  4   |
|    1    |    24   |      0.0       |  5   |
|    1    |    25   |      0.0       |  6   |
|    2    |    26   |      0.0       |  6   |
|    2    |    24   |      0.0       |  5   |
|    2    |    23   |      0.0       |  4   |
|    2    |    21   |      0.0       |  3   |
|    2    |    25   |      0.5       |  2   |
|    2    |    22   |      0.5       |  1   |
|    3    |    24   |      0.0       |  6   |
|    3    |    25   | 0.166666666667 |  5   |
|    3    |    22   | 0.166666666667 |  4   |
|    3    |    26   | 0.333333333333 |  3   |
|    3    |    23   | 0.333333333333 |  2   |
|    3    |    21   | 0.333333333333 |  1   |
+---------+---------+----------------+------+
[18 rows x 4 columns]
>>> # we add recommended items not owned by user to the original SFrame
>>> rec = rec_wo_own_item.groupby('user_id', {'reco':gl.aggregate.CONCAT('item_id')})
>>> sf = sf.join(rec, 'user_id', 'left')
>>> print sf
+---------+---------+----------------------+
| item_id | user_id |         reco         |
+---------+---------+----------------------+
|    21   |    1    |     [24, 25, 22]     |
|    22   |    2    |   [24, 26, 23, 21]   |
|    23   |    1    |     [24, 25, 22]     |
|    24   |    3    | [21, 23, 26, 25, 22] |
|    25   |    2    |   [24, 26, 23, 21]   |
|    26   |    1    |     [24, 25, 22]     |
+---------+---------+----------------------+
[6 rows x 3 columns]

How to handle when we have additional boolean column is_purchased which is true if the user has purchased that item otherwise false. In the doc which you mentioned I see a exclude argument, should I add this arg with SFrame( user who has already purchased the item ) to handle the case. — Prashant Bhanarkar, Sep 04 '16 at 10:47

graphlab don't recommend items already owned by user

1 Answers1

Example