Arguments - Aster Analytics

Teradata Aster Analytics Foundation User Guide

Product
Aster Analytics
Release Number
6.21
Published
November 2016
Language
English (United States)
Last Update
2018-04-14
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kiu1466024880662.ditamap
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AA-notempfilter_pdf_output.ditaval
dita:id
B700-1021
lifecycle
previous
Product Category
Software
Argument Category Description
RatingTable Required Specifies the name of the user rating table.
UserIDColumn Optional Specifies the user_rating_table column that contains the user IDs. The default is the first column in the rating table.
ItemIDColumn Optional Specifies the user_rating_table column that contains the item IDs. The default is the second column in the rating table.
RatingColumn Optional Specifies the user_rating_table column that contains the ratings. The default is the third column in the rating table.
WeightModelTable Required Specifies the name for the output table of interpolation weights.
BiasModelTable Required Specifies the name for the output table of global, user, and item bias statistics.
NearestItemsTable Optional Specifies the name for the output table of nearest neighbors for each item.

If you omit this argument, the function does not output the NearestNeighbors table.

If you specify this argument, and a table named item_neighbors_table exists, the function uses the existing table to train the model.

If you specify this argument, and no table named item_neighbors_table exists, the function creates a table with that name.

K Optional Specifies the number of nearest neighbors with which to calculate the interpolation weights. The default value is 20.
LearningRate Optional Specifies the initial learning rate. The learning rate adjusts automatically during training based on changes in the root-mean-square error (RMSE). The default value is 0.001.
MaxIterNum Optional Specifies the maximum number of iterations. The default value is 10.
Threshold Optional Specifies the RMSE below which the function stops. The default value is 0.0002.
ItemSimilarity Optional Specifies the method for calculating item similarity. The default is the Pearson correlation coefficient:
  • 'pearson': Pearson correlation coefficient (default)


  • 'adjustedcosine': adjusted cosine similarity