BPR Recommender Jobs

Use this job when you want to compute user recommendations or item similarities using a Bayesian Personalized Ranking (BPR) recommender algorithm.

This job assumes that your signals collection contains the preferences of many users. It uses this collection of preferences to predict another user’s preference about an item that the user has not yet seen. A preference which can be viewed as a triple:

  • user - Use Training Collection User Id Field to specify the name of the user ID field, usually user_id_s.

  • item - Use Training Collection Item Id Field to specify the name of the item ID field, usually item_id_s.

  • interaction-value - Use Training Collection Counts/Weights Field to specify the name of the interaction value field, usually aggr_count_i.

Compared to ALS-based recommenders, BPR-based recommenders compare a pair of recommendations for a user instead of static 0, 1 input-based recommendations as in ALS.

Note
You can also configure this job to read from or write to cloud storage. See Configure An Argo-Based Job to Access GCS and Configure An Argo-Based Job to Access S3.
BPR collaborative recommendations dataflow

BPR dataflow

Note
If using solr as the training data source ensure that the source collection contains the random_* dynamic field defined in its managed-schema. This field is required for sampling the data. If it is not present, add the following entry to the managed-schema alongside other dynamic fields <dynamicField name="random_*" type="random"/> and <fieldType class="solr.RandomSortField" indexed="true" name="random"/> alongside other field types.

Tuning tips

The BPR Recommender job has a few unique tuning parameters compared to the ALS Recommender job:

  • Training Data Filtered By Popular Items

    By setting the minimum number of user interactions required for items to be included in training and recommendations, you can suppress items that don’t yet have enough signals data for meaningful recommendations.

  • Filter already clicked items

    This feature produces only "fresh" recommendations, by omitting items the user has already clicked. (It also increases the job’s running time.)

  • Perform approximate nearest neighbor search

    This option reduces the job’s running time significantly, with a small decrease in accuracy. If your training dataset is very small, then you can disable this option.

  • Evaluate on test data

    This feature samples the original dataset to evaluate how well the trained model predicts unseen user interactions. The clicks that are sampled for testing are not used for training. For example, with the default configuration, users who have at least three total clicks are selected for testing. For each of those users, one click is used for testing and the rest are used for training. The trained model is applied to the test data, and the evaluation results are written to the log.

  • Metadata fields for item-item evaluation

    These fields are used during evaluation to determine whether pairs belong to the same category.

Query pipeline setup