Collaborative Filter Recommender

Using the model trained by the Collaborative Filter Trainer, outputs recommendations for those users or products.

Information at a Glance

Category	Predict
Data source type	HD
Sends output to other operators	Yes
Data processing tool	Spark

Input

The output from a Collaborative Filter Trainer, as well as an HDFS data set.

Restrictions

The column selected must represent either user IDs or product IDs.

Configuration

Parameter	Description
Notes	Any notes or helpful information about this operator's parameter settings. When you enter content in the Notes field, a yellow asterisk is displayed on the operator.
Generate Recommendations for	Select a column in your data set that represents either users or product IDs. To see a list of products that a user might like, choose User (the default). To see a list of users who might like a product, choose Product.
This Column Represents	Indicates what the Generate Recommendations for column represents - Users (the default) or Products.
Number to Recommend	Specify the number of recommendations to generate. Range: 1-100. Default value: 5.
Output Directory	The location to store the output files.
Output Name	The name to contain the results.
Overwrite Output	Specifies whether to delete existing data at that path. Yes - if the path exists, delete that file and save the results. No - fail if the path already exists.
Storage Format	Select the format in which to store the results. The storage format is determined by your type of operator. Typical formats are Avro, CSV, TSV, or Parquet.
Compression	Select the type of compression for the output. Available Parquet compression options. GZIP Deflate Snappy no compression Available Avro compression options. Deflate Snappy no compression
Advanced Spark Settings Automatic Optimization	Yes specifies using the default Spark optimization settings. No enables providing customized Spark optimization. Click Edit Settings to customize Spark optimization. See Advanced Settings Dialog Box for more information.

Output

Visual Output

This output shows each user ID in the users column selected, then lists five products they might enjoy and their predicted ratings.

Data Output

This data can be further manipulated by other operators in your workflow. It is passed on as a tabular HDFS data set. You can find the storage location of the recommendation table by referring to the Summary section of the results pane, as shown in the following example.