1.1 - 8.10 - WSRecommender Input - Teradata Vantage

Teradata Vantage™ - Machine Learning Engine Analytic Function Reference

Product
Teradata Vantage
Release Number
1.1
8.10
Release Date
October 2019
Content Type
Programming Reference
Publication ID
B700-4003-079K
Language
English (United States)

ItemTable Schema

This table contains all pairs of items bought together. Typically, the CFilter function outputs this table.

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 (ML Engine) 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.

UserTable Schema

This table contains the user preference for each item. The function recalculates these preferences, using the preferences of other items and how many times the item was bought with those items.

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.