1.0 - 8.00 - WSRecommender Input - Teradata Vantage

Teradata® Vantage Machine Learning Engine Analytic Function Reference

Product
Teradata Vantage
Release Number
1.0
8.00
Release Date
May 2019
Content Type
Programming Reference
Publication ID
B700-4003-098K
Language
English (United States)

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.