API Reference
Welcome to the API Reference. This section contains the documentation of the
functions and classes of ROSE.
And modules built by Cornac can be found here.
API Reference
- Data
- Datasets
- Models
- Metrics for Recommender
- Explainers
- Explainer(Generic Class)
- Explainer for Alternating Least Squares of Matrix Factorization for Implicit Feedback Datasets (ALS)
- Counterfactual Explainable Recommendation (Counter)
- Explainer for Explicit Factor Model (EFM_Mod)
- Explainer for Explicit Factor Model (EFM)
- Explainers for Explainable Recommendation with Comparative Constraints on Product Aspects (ComparER)
- Explainer for Explainable Matrix Factorization (EMF)
- Explainer for Personalised novel and explainable matrix factorisation (NEMF)
- Locally Interpretable Model-agnostic Explanations (LIMERS)
- Preference-based and local post-hoc explanations for recommender systems (LIRE)
- Explainable Recommendation via Multi-Task Learning in Opinionated Text Data (MTER)
- Post-Hoc Explanation for Matrix Factorization (PHI4MF)
- Provider-side Interpretability with Counterfactual Explanations in Recommender Systems (PRINCE)
- Similar User Explanation for EMF (SU4EMF)
- Explainer for TriRank
- Explainer for Neural Attentional Rating Regression with Review-level Explanations (NARRE)
- Metrics for Explanations
- Metrics(Generic Class)
- Explainable normalised Discounted Cumulative Gain(EnDCG)
- Feature Agreement(FA)
- Feature Diversity(DIV)
- Precision, Recall, and their harmonic mean(FPR)
- Mean Explnalibility Precision(MEP)
- Prediction Gap Fidelity(PGF)
- Probability of necessity (PN), probability of sufficiency (PS) and their harmonic mean (FNS)
- Rank Agreement(RA)
- Experiment
- Evaluation Methods
- Hyper-parameter Tuning