vastorbit.VastFrame.recommend

VastFrame.recommend(unique_id: str, item_id: str, method: Literal['count', 'avg', 'median'] = 'count', rating: str | tuple = '', ts: str | None = None, start_date: Annotated[bool | float | str | timedelta | datetime, 'Python Scalar'] = '', end_date: Annotated[bool | float | str | timedelta | datetime, 'Python Scalar'] = '') → VastFrame

Recommend items based on the Collaborative Filtering (CF) technique. The implementation is the same as APRIORI algorithm, but is limited to pairs of items.

Parameters:

• unique_id (str) – Input VastColumn corresponding to a unique ID. It serves as a primary key in another dataset. In our context, it represents an operation, such as a basket ID, which includes multiple sub-transactions.

• item_id (str) – Input VastColumn corresponding to an item ID. It is a secondary key used to compute the different pairs.

• method (str, optional) –

  Method used to recommend.

  • count:

    Each item will be recommended based on frequencies of the different pairs of items.

  • avg:

    Each item will be recommended based on the average rating of the different item pairs with a differing second element.

  • median:

    Each item will be recommended based on the median rating of the different item pairs with a differing second element.

• rating (str | tuple, optional) –

  Input VastColumn including the items rating. If the rating type is tuple, it must be composed of 3 elements:

  (r_vdf, r_item_id, r_name) where:

  r_vdf is an input VastFrame.

  r_item_id is an input VastColumn which must includes the same id as item_id.

  r_name is an input VastColumn including the items rating.

• ts (str, optional) – TS (Time Series) VastColumn used to order the data. The VastColumn type must be date (date, datetime, timestamp…) or numerical.

• start_date (str | PythonNumber | date, optional) – Input Start Date. For example, time = '03-11-1993' will filter the data when ts is less than November 1993 the 3rd.

• end_date (str | PythonNumber | date, optional) – Input End Date. For example, time = '03-11-1993' will filter the data when ts is greater than November 1993 the 3rd.

Returns:

The VastFrame of the recommendation.

Return type:

VastFrame

Examples

Let’s begin by importing vastorbit.

import vastorbit as vo

Hint

By assigning an alias to vastorbit, we mitigate the risk of code collisions with other libraries. This precaution is necessary because vastorbit uses commonly known function names like “average” and “median”, which can potentially lead to naming conflicts. The use of an alias ensures that the functions from vastorbit are used as intended without interfering with functions from other libraries.

Let us create a VastFrame which has some purchase transaction data:

• transaction_id:

  Unique ID for a transaction.

• item_id:

  The unique ID for different items that were purchased.

• rating:

  Rating provided by the user for the item purchased.

vdf = vo.VastFrame(
    {
        "transaction_id": [1, 1, 1, 2, 2, 3, 3, 3],
        "item_id": ["A", "B", "C", "B", "C", "A", "B", "C"],
        "rating": [8, 5, 1, 6, 2, 9, 4, 3],
    },
)

	123 transaction_id Integer	Abc item_id Varchar(1)	123 rating Integer
1	3	A	9
2	3	B	4
3	2	C	2
4	1	C	1
5	3	C	3
6	2	B	6
7	1	A	8
8	1	B	5

Rows: 1-8 | Columns: 3

We can easily create the recommend table from the above data:

recommendations = vdf.recommend(
    unique_id = "transaction_id",
    item_id = "item_id",
    method = "avg",
    rating = "rating",
)

	Abc item1 Varchar(1)	Abc item2 Varchar(1)	123 cnt Bigint	123 score1 Double	123 score2 Double	123 rank Bigint
1	A	B	2	8.5	5.0	1
2	A	C	2	8.5	2.0	2
3	B	A	2	5.0	8.5	1
4	B	C	3	5.0	2.0	2
5	C	A	2	2.0	8.5	1
6	C	B	3	2.0	5.0	2

Rows: 1-6 | Columns: 6

Note

This function is highly useful for basket analysis and can be employed to derive valuable recommendations.

Let’s look at another example involving timestamp values:

# Create a VastFrame with the transaction data
vdf = vo.VastFrame(
    {
        "transaction_id": [1, 1, 1, 2, 2, 3, 3, 3],
        "item_id": ["A", "B", "C", "B", "C", "A", "B", "C"],
        "rating": [8, 5, 1, 6, 2, 9, 4, 3],
        "date": [
            "2021-1-1",
            "2021-1-1",
            "2021-1-1",
            "2021-1-4",
            "2021-1-4",
            "2021-1-21",
            "2021-1-21",
            "2021-1-21",
        ],
    },
)

	123 transaction_id Integer	Abc item_id Varchar(1)	123 rating Integer	Abc date Varchar(9)
1	3	A	9	2021-1-21
2	1	C	1	2021-1-1
3	1	B	5	2021-1-1
4	3	C	3	2021-1-21
5	2	B	6	2021-1-4
6	2	C	2	2021-1-4
7	1	A	8	2021-1-1
8	3	B	4	2021-1-21

Rows: 1-8 | Columns: 4

Then we can use the timestamp column to filter the recommendation results:

recommendations = vdf.recommend(
    unique_id = "transaction_id",
    item_id = "item_id",
    method = "avg",
    rating = "rating",
    ts = "date",
    start_date = "2021-1-1",
    end_date = "2021-1-5",
)

	Abc item1 Varchar(1)	Abc item2 Varchar(1)	123 cnt Bigint	123 score1 Double	123 score2 Double	123 rank Bigint
1	B	A	2	5.0	8.5	1
2	B	C	3	5.0	2.0	2
3	A	B	2	8.5	5.0	1
4	A	C	2	8.5	2.0	2
5	C	A	2	2.0	8.5	1
6	C	B	3	2.0	5.0	2

Rows: 1-6 | Columns: 6

See also

VastFrame.add_duplicates() : Add duplicates of values based on weights.