This is an Unbiased Learning To Rank Algorithms (ULTRA) toolbox, which provides a codebase for experiments and research on learning to rank with human annotated or noisy labels. With the unified data processing pipeline, ULTRA supports multiple unbiased learning-to-rank algorithms, online learning-to-rank algorithms, neural learning-to-rank models, as well as different methods to use and simulate noisy labels (e.g., clicks) to train and test different algorithms/ranking models. A user-friendly documentation can be found here.

### Input Layers

ClickSimulationFeed: this is the input layer that generate synthetic clicks on fixed ranked lists to feed the learning algorithm.

DeterministicOnlineSimulationFeed: this is the input layer that first create ranked lists by sorting documents according to the current ranking model, and then generate synthetic clicks on the lists to feed the learning algorithm. It can do result interleaving if required by the learning algorithm.

StochasticOnlineSimulationFeed: this is the input layer that first create ranked lists by sampling documents based on their scores in the current ranking model and the Plackett-Luce distribution, and then generate synthetic clicks on the lists to feed the learning algorithm. It can do result interleaving if required by the learning algorithm.

DirectLabelFeed: this is the input layer that directly feed the true relevance labels of each documents to the learning algorithm.

### Learning Algorithms

DLA: this is an implementation of the Dual Learning Algorithm in

*Unbiased Learning to Rank with Unbiased Propensity Estimation*.IPW: this model is an implementation of the Inverse Propensity Weighting algorithms in

*Learning to Rank with Selection Bias in Personal Search*and*Unbiased Learning-to-Rank with Biased Feedback*REM: this model is an implementation of the regression-based EM algorithm in

*Position bias estimation for unbiased learning to rank in personal search*PD: this model is an implementation of the pairwise debiasing algorithm in

*Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm*.PDGD: this model is an implementation of the Pairwise Differentiable Gradient Descent algorithm in

*Differentiable unbiased online learning to rank*DBGD: this model is an implementation of the Dual Bandit Gradient Descent algorithm in

*Interactively optimizing information retrieval systems as a dueling bandits problem*NA: this model is an implementation of the naive algorithm that directly train models with input labels (e.g., clicks).

### Ranking Models

Linear: this is a linear ranking algorithm that compute ranking scores with a linear function.

DNN: this is neural ranking algorithm that compute ranking scores with a multi-layer perceptron network (with non-linear activation functions).

DLCM: this is an implementation of the Deep Listwise Context Model in

*Learning a Deep Listwise Context Model for Ranking Refinement*.GSF: this is an implementation of the Groupwise Scoring Function in

*Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks*.SetRank: this is an implementation of the SetRank model in

*SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval*.

## Supported Evaluation Metrics

MRR: the Mean Reciprocal Rank (inherited from TF-Ranking).

ERR: the Expected Reciprocal Rank from

*Expected reciprocal rank for graded relevance*.ARP: the Average Relevance Position (inherited from TF-Ranking).

NDCG: the Normalized Discounted Cumulative Gain (inherited from TF-Ranking).

DCG: the Discounted Cumulative Gain (inherited from TF-Ranking).

Precision: the Precision (inherited from TF-Ranking).

MAP: the Mean Average Precision (inherited from TF-Ranking).

Ordered_Pair_Accuracy: the percentage of correctedly ordered pair (inherited from TF-Ranking).