Learning-To-Rank

174 papers with code • 0 benchmarks • 9 datasets

Learning to rank is the application of machine learning to build ranking models. Some common use cases for ranking models are information retrieval (e.g., web search) and news feeds application (think Twitter, Facebook, Instagram).

Libraries

Use these libraries to find Learning-To-Rank models and implementations

RankingSHAP -- Listwise Feature Attribution Explanations for Ranking Models

mariaheuss/rankingshap 24 Mar 2024

We evaluate RankingSHAP for commonly used learning-to-rank datasets to showcase the more nuanced use of an attribution method while highlighting the limitations of selection-based explanations.

3
24 Mar 2024

Metasql: A Generate-then-Rank Framework for Natural Language to SQL Translation

Kaimary/MetaSQL 27 Feb 2024

While these translation models have greatly improved the overall translation accuracy, surpassing 70% on NLIDB benchmarks, the use of auto-regressive decoding to generate single SQL queries may result in sub-optimal outputs, potentially leading to erroneous translations.

3
27 Feb 2024

Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from Large Language Models

pxyu/llm-nle-for-calibration 19 Feb 2024

The process of scale calibration in ranking systems involves adjusting the outputs of rankers to correspond with significant qualities like click-through rates or relevance, crucial for mirroring real-world value and thereby boosting the system's effectiveness and reliability.

1
19 Feb 2024

List-aware Reranking-Truncation Joint Model for Search and Retrieval-augmented Generation

xsc1234/genrt 5 Feb 2024

First, it is hard to share the contextual information of the ranking list between the two tasks.

2
05 Feb 2024

How to Forget Clients in Federated Online Learning to Rank?

ielab/2024-ecir-foltr-unlearning 24 Jan 2024

In a FOLTR system, a ranker is learned by aggregating local updates to the global ranking model.

2
24 Jan 2024

Learning-To-Rank Approach for Identifying Everyday Objects Using a Physical-World Search Engine

keio-smilab23/multirankit 26 Dec 2023

Therefore, we focus on the task of retrieving target objects from open-vocabulary user instructions in a human-in-the-loop setting, which we define as the learning-to-rank physical objects (LTRPO) task.

0
26 Dec 2023

SARDINE: A Simulator for Automated Recommendation in Dynamic and Interactive Environments

naver/sardine 28 Nov 2023

Simulators can provide valuable insights for researchers and practitioners who wish to improve recommender systems, because they allow one to easily tweak the experimental setup in which recommender systems operate, and as a result lower the cost of identifying general trends and uncovering novel findings about the candidate methods.

4
28 Nov 2023

GLEN: Generative Retrieval via Lexical Index Learning

skleee/GLEN 6 Nov 2023

For training, GLEN effectively exploits a dynamic lexical identifier using a two-phase index learning strategy, enabling it to learn meaningful lexical identifiers and relevance signals between queries and documents.

17
06 Nov 2023

RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Active Data Manipulation

dzungvpham/raifle 29 Oct 2023

Federated learning (FL) has recently emerged as a privacy-preserving approach for machine learning in domains that rely on user interactions, particularly recommender systems (RS) and online learning to rank (OLTR).

1
29 Oct 2023

Learning to Rank Context for Named Entity Recognition Using a Synthetic Dataset

CompNet/conivel 16 Oct 2023

Using this dataset, we train a neural context retriever based on a BERT model that is able to find relevant context for NER.

4
16 Oct 2023