Learning-To-Rank

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

9 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 213 papers with code (753 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
  19 Aug 2020 12 repositories listed Syntology ran 2 of 14 samples · 12 unverified
  Learning effective feature crosses is the key behind building recommender systems.
• Improving Pairwise Ranking for Multi-label Image Classification
  11 Apr 2017 4 repositories listed
  Pairwise ranking, in particular, has been successful in multi-label image classification, achieving state-of-the-art results on various benchmarks.
• Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering
  10 Oct 2017 3 repositories listed
  In this paper, we propose a novel end-to-end neural architecture for ranking candidate answers, that adapts a hierarchical recurrent neural network and a latent topic clustering module.
• Support vector comparison machines
  30 Jan 2014 3 repositories listed
  In ranking problems, the goal is to learn a ranking function from labeled pairs of input points.
• Introducing LETOR 4.0 Datasets
  9 Jun 2013 3 repositories listed
  We call the two query sets MQ2007 and MQ2008 for short.
• Learning to Rank Aspects and Opinions for Comparative Explanations
  16 Jan 2025 2 repositories listed
  Comparative recommendation explanations help to make sense of recommendations by comparing a recommended item along some aspects of interest with one or many items being considered.
• Offline Model-Based Optimization by Learning to Rank
  15 Oct 2024 2 repositories listed Syntology ran 15 of 17 samples · 2 unverified · 9 pointer-only (licence)
  Based on this observation, we propose learning a ranking-based model that leverages learning to rank techniques to prioritize promising designs based on their relative scores.
• Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset
  3 Apr 2024 2 repositories listed
  Our experiments reveal that gains in click prediction do not necessarily translate to enhanced ranking performance on expert relevance annotations, implying that conclusions strongly depend on how success is measured in…
• Learning to Rank in Generative Retrieval
  27 Jun 2023 2 repositories listed
  However, only learning to generate is insufficient for generative retrieval.
• Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification
  24 Jun 2023 2 repositories listed Syntology ran 9 of 23 samples · 14 unverified
  Consequently, we propose a cross-modal ordinal pairwise loss to refine the CLIP feature space, where texts and images maintain both semantic alignment and ordering alignment.
• Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective
  13 Jun 2023 2 repositories listed
  Building upon this, we leverage offline RL techniques for off-policy LTR and propose the Click Model-Agnostic Unified Off-policy Learning to Rank (CUOLR) method, which could be easily applied to a wide range of click…
• THUIR@COLIEE 2023: More Parameters and Legal Knowledge for Legal Case Entailment
  11 May 2023 2 repositories listed
  This paper describes the approach of the THUIR team at the COLIEE 2023 Legal Case Entailment task.
• THUIR@COLIEE 2023: Incorporating Structural Knowledge into Pre-trained Language Models for Legal Case Retrieval
  11 May 2023 2 repositories listed
  Legal case retrieval techniques play an essential role in modern intelligent legal systems.
• An Offline Metric for the Debiasedness of Click Models
  19 Apr 2023 2 repositories listed
  A well-known problem when learning from user clicks are inherent biases prevalent in the data, such as position or trust bias.
• Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-oriented Dialogue Systems
  20 Feb 2023 2 repositories listed Syntology ran 1 of 11 samples · 10 unverified
  Prior works mainly focus on adopting advanced RL techniques to train the ToD agents, while the design of the reward function is not well studied.
• Pairwise Learning for Neural Link Prediction
  6 Dec 2021 2 repositories listed Syntology ran 1 of 3 samples · 2 unverified · 3 pointer-only (licence)
  The framework treats link prediction as a pairwise learning to rank problem and consists of four main components, i.
• An Efficient Approach for Cross-Silo Federated Learning to Rank
  21 Jun 2021 2 repositories listed
  Traditional learning-to-rank (LTR) models are usually trained in a centralized approach based upon a large amount of data.
• On the Relationship between Explanation and Recommendation: Learning to Rank Explanations for Improved Performance
  1 Feb 2021 2 repositories listed
  Explaining to users why some items are recommended is critical, as it can help users to make better decisions, increase their satisfaction, and gain their trust in recommender systems (RS).
• On the Problem of Underranking in Group-Fair Ranking
  24 Sep 2020 2 repositories listed
  We give a fair ranking algorithm that takes any given ranking and outputs another ranking with simultaneous underranking and group fairness guarantees comparable to the lower bound we prove.
• Ranking-Incentivized Quality Preserving Content Modification
  26 May 2020 2 repositories listed
  The Web is a canonical example of a competitive retrieval setting where many documents' authors consistently modify their documents to promote them in rankings.
• Distance-based Positive and Unlabeled Learning for Ranking
  20 May 2020 2 repositories listed
  Learning to rank -- producing a ranked list of items specific to a query and with respect to a set of supervisory items -- is a problem of general interest.
• That is a Known Lie: Detecting Previously Fact-Checked Claims
  12 May 2020 2 repositories listed
  Interestingly, despite the importance of the task, it has been largely ignored by the research community so far.
• SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval
  12 Dec 2019 2 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  In learning-to-rank for information retrieval, a ranking model is automatically learned from the data and then utilized to rank the sets of retrieved documents.
• To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions
  15 Jul 2019 2 repositories listed
  At the moment, two methodologies for dealing with bias prevail in the field of LTR: counterfactual methods that learn from historical data and model user behavior to deal with biases; and online methods that perform…
• Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank
  17 Feb 2019 2 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and…
• TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank
  30 Nov 2018 2 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  We propose TensorFlow Ranking, the first open source library for solving large-scale ranking problems in a deep learning framework.
• Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks
  11 Nov 2018 2 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  To overcome this limitation, we propose a new framework for multivariate scoring functions, in which the relevance score of a document is determined jointly by multiple documents in the list.
• Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources
  6 Nov 2017 2 repositories listed
  In this framework, each type of information source (review text, product image, numerical rating, etc) is adopted to learn the corresponding user and item representations based on available (deep) representation…
• Learning Latent Vector Spaces for Product Search
  25 Aug 2016 2 repositories listed
  We introduce a novel latent vector space model that jointly learns the latent representations of words, e-commerce products and a mapping between the two without the need for explicit annotations.
• Kamae: Bridging Spark and Keras for Seamless ML Preprocessing
  8 Jul 2025 1 repository listed
  In production recommender systems, feature preprocessing must be faithfully replicated across training and inference environments.

Syntology lines on 9 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.