Welcome to RePlay’s documentation!

Contents:

• Installation
• Development
  • Adding new model
• Modules
  • Data
    • Dataset
      • Dataset
    • DatasetLabelEncoder
      • DatasetLabelEncoder
    • FeatureType
      • FeatureType
    • FeatureSource
      • FeatureSource
    • FeatureHint
      • FeatureHint
    • FeatureInfo
      • FeatureInfo
    • FeatureSchema
      • FeatureSchema
    • GetSchema
      • get_schema()
    • Neural Networks
      • TensorFeatureInfo
      • TensorFeatureSource
      • TensorSchema
      • SequenceTokenizer
      • PandasSequentialDataset
      • TorchSequentialBatch
      • TorchSequentialDataset
      • TorchSequentialValidationBatch
      • TorchSequentialValidationDataset
  • Preprocessing
    • Filters
      • EntityDaysFilter
      • GlobalDaysFilter
      • InteractionEntriesFilter
      • LowRatingFilter
      • MinCountFilter
      • NumInteractionsFilter
      • TimePeriodFilter
    • CSRConverter
      • CSRConverter
    • Sessionizer
      • Sessionizer
    • Padder (Experimental)
      • Padder
    • SequenceGenerator (Experimental)
      • SequenceGenerator
    • Data Preparation (Experimental)
      • Indexer
      • DataPreparator
  • Splitters
    • split()
    • TwoStageSplitter
      • TwoStageSplitter
    • KFolds
      • KFolds()
    • TimeSplitter
      • TimeSplitter
    • LastNSplitter
      • LastNSplitter
    • RatioSplitter
      • RatioSplitter
    • RandomSplitter
      • RandomSplitter
    • NewUsersSplitter
      • NewUsersSplitter
    • ColdUserRandomSplitter
      • ColdUserRandomSplitter
  • Models
    • RePlay Recommenders
    • Recommender interface
      • Recommender
    • Distributed models
      • Popular Recommender
      • Query Popular Recommender
      • Wilson Recommender
      • Random Recommender
      • UCB Recommender
      • KL-UCB Recommender
      • Thompson Sampling
      • K Nearest Neighbours
      • Alternating Least Squares
      • Alternating Least Squares on Scala (Experimental)
      • SLIM
      • Word2Vec Recommender
      • Association Rules Item-to-Item Recommender
      • Cluster Recommender
    • Neural models with distributed inference
      • Neural Matrix Factorization (Experimental)
      • Mult-VAE (Experimental)
      • DDPG (Experimental)
      • DT4Rec (Experimental)
      • CQL Recommender (Experimental)
    • Wrappers and other models with distributed inference
      • ADMM SLIM (Experimental)
      • LightFM (Experimental)
      • implicit (Experimental)
    • Neural Networks recommenders
      • Bert4Rec
      • SasRec
  • Metrics
    • Precision
      • Precision
    • Recall
      • Recall
    • MAP
      • MAP
    • MRR
      • MRR
    • NDCG
      • NDCG
    • HitRate
      • HitRate
    • RocAuc
      • RocAuc
    • Unexpectedness
      • Unexpectedness
    • Coverage
      • Coverage
    • CategoricalDiversity
      • CategoricalDiversity
    • Novelty
      • Novelty
    • Surprisal
      • Surprisal
    • OfflineMetrics
      • OfflineMetrics
    • Compare Results
      • Experiment
    • Custom Metric
      • Metric
  • Scenarios
    • Fallback
      • Fallback
    • Two Stage Scenario (Experimental)
      • TwoStagesScenario
    • Ofline Policy Learners
      • OBPOfflinePolicyLearner
  • Utils
    • Time Smoothing
      • smoothe_time()
      • get_item_recency()
    • Serializer
      • save()
      • load()
    • Distributions
      • Item Distribution
• Settings
  • Spark session
    • get_spark_session()
    • State
  • Logging
• Useful Info
  • How to choose a recommender
    • Input Data
      • What is the input?
      • Are interactions explicit?
      • Will there be new users?
      • Will there be new items?
    • Model requirements
      • Should recommendations be personalized?
      • Should cold users get recommendations? (without any interactions).
      • Should model recommend cold items? (that no one interacted with).
      • Should model be able to recommend unseen items?
    • Model Comparison
      • MovieLens 1m
  • How to optimize hyper parameters
    • optimize()

Indices and tables

• Index

• Module Index

• Search Page