Learners : Matchbox recommender : Command-line runners
Predictors
The Matchbox recommender in Infer.NET supports 4 modes of prediction - rating prediction, item recommendation, find related users, and find related items. There is a command-line runner for each one of these.
Rating predictor
Rating prediction is performed using the PredictRatings argument to Learner Recommender. It takes in a test set and a model, and produces a file of predictions.
Required parameters
- data - dataset to make predictions for; typically produced by the command-line data splitter
- model - file containing a trained model produced by the command-lined trainer
- predictions - the file containing the generated predictions
Example
Learner Recommender PredictRatings --data TestSet.dat
--model TrainedRecommender.bin
--predictions RatingPredictions.dat
Item recommender
Item recommendation is performed using the RecommendItems argument to Learner Recommender. It takes in a test set and a model, and produces a file of predictions. Note that items are recommended with the intention to be later evaluated, so this module follows the prediction procedure explained here. That is, items are recommended for each unique user in the test set, and predictions are restricted to the items that each user has rated.
Required parameters
- data - dataset to make predictions for; typically produced by the command-line data splitter
- model - file containing a trained model produced by the command-lined trainer
- predictions - the file containing the generated predictions
Optional parameters
- max-items - maximum number of items to recommend; defaults to 5
- min-pool-size - minimum size of the recommendation pool for a single user; defaults to 5
Example
Learner Recommender RecommendItems --data TestSet.dat
--model TrainedRecommender.bin
--predictions ItemRecommendations.dat
--max-items 10
Related user finder
Related users can be found using the FindRelatedUsers argument to Learner Recommender. It takes in a test set and a model, and produces a file of predictions. Note that related users are found with the intention to be later evaluated, so this module follows the prediction procedure explained here. That is, related users are found for users who have rated a given number of items in common.
Required parameters
- data - dataset to make predictions for; typically produced by the command-line data splitter
- model - file containing a trained model produced by the command-lined trainer
- predictions - the file containing the generated predictions
Optional parameters
- max-users - maximum number of related users for a single user; defaults to 5
- min-common-items - minimum number of items that the query user and the related user should have rated in common; defaults to 5
- min-pool-size - minimum size of the related user pool for a single user; defaults to 5
Example
Learner Recommender FindRelatedUsers --data TestSet.dat
--model TrainedRecommender.bin
--predictions RelatedUsers.dat
--max-users 10
--min-common-items 2
Related item finder
Related items can be found using the FindRelatedItems argument to Learner Recommender. It takes in a test set and a model, and produces a file of predictions. Note that related items are found with the intention to be later evaluated, so this module follows the prediction procedure explained here. That is, related items are found for items which have been rated by a given number of users in common.
Required parameters
- data - dataset to make predictions for; typically produced by the command-line data splitter
- model - file containing a trained model produced by the command-lined trainer
- predictions - the file containing the generated predictions
Optional parameters
- max-items - maximum number of related items for a single item; defaults to 5
- min-common-users - minimum number of users that the query item and the related item should have been rated by in common; defaults to 5
- min-pool-size - minimum size of the related item pool for a single item; defaults to 5
Example
Learner Recommender FindRelatedItems --data TestSet.dat
--model TrainedRecommender.bin
--predictions RelatedItems.dat
--max-items 10
--min-common-users 2