item_table Schema
| Column | Data Type | Description |
|---|---|---|
| item1_column | VARCHAR | First item (item1). Column by which table is partitioned. The database handles NULL values in partitioning columns. You need not exclude them with a WHERE clause.
|
| item2_column | VARCHAR | Second item (item2). |
| similarity_column | INTEGER or DOUBLE PRECISION | Similarity score for item1 and item2 (number of times item1_column co-occurs with item2_column). To compute this score, use CFilter function. |
| accumulate_item_column | Any | [Column appears once for each specified accumulate_item_column.] Column to copy to output table. |
This table must be symmetric with respect to item1_column and item2_column. That is, if a row has 'apple' in item1_column and 'bread' in item2_column, then another row must have 'bread' in item1_column and 'apple' in item2_column, and these two rows must have the same value in similarity_column.
The function gives the best results when the items in item1_column and item2_column satisfy triangular inequality; that is: if a row has 'apple' in item1_column and 'bread' in item2_column, then another row must have 'bread' in item1_column and 'apple' in item2_column, and these two rows must have the same value in similarity_column.
user_table Schema
| Column | Data Type | Description |
|---|---|---|
| item_column | VARCHAR | Name of item that user viewed or bought. Column by which table is partitioned. The database handles NULL values in partitioning columns. You need not exclude them with a WHERE clause.
|
| user_column | VARCHAR | User identifier. |
| preference_column | INTEGER | User preference score for item. |
| accumulate_user_column | Any | [Column appears once for each specified accumulate_user_column.] Column to copy to output table. |