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The recent representative methods (such as MO4SRD and DALETOR) for Search Result Diversification by directly optimizing the evaluation metric (e.g., alpha-nDCG) have been added. (02/22/2022)
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Different types of neural scoring functions are supported now, namely pointwise neural scoring function (mainly consists of feedforward layers) and listwise neural scoring function (mainly builds upon multi-head self-attention Layer). (02/22/2022)
This open-source project, referred to as PTRanking (Learning-to-Rank in PyTorch) aims to provide scalable and extendable implementations of typical learning-to-rank methods based on PyTorch. On one hand, this project enables a uniform comparison over several benchmark datasets leading to an in-depth understanding of previous learning-to-rank methods. On the other hand, this project makes it easy to develop and incorporate newly proposed models, so as to expand the territory of techniques on learning-to-rank.
Key Features:
- A number of representative learning-to-rank models for addressing Ad-hoc Ranking and Search Result Diversification, including not only the traditional optimization framework via empirical risk minimization but also the adversarial optimization framework
- Supports widely used benchmark datasets. Meanwhile, random masking of the ground-truth labels with a specified ratio is also supported
- Supports different metrics, such as Precision, MAP, nDCG, nERR, alpha-nDCG and ERR-IA.
- Highly configurable functionalities for fine-tuning hyper-parameters, e.g., grid-search over hyper-parameters of a specific model
- Provides easy-to-use APIs for developing a new learning-to-rank model
Please refer to the documentation site for more details.