Papers Learning-To-Rank
“Learning-To-Rank” 태그가 달린 논문 753편 · 필터 해제
Kamae: Bridging Spark and Keras for Seamless ML Preprocessing
In production recommender systems, feature preprocessing must be faithfully replicated across training and inference environments. This often requires duplicating logic between offline and online environments, increasing…
Learning-To-RankRecommendation SystemsUnidentified and Confounded? Understanding Two-Tower Models for Unbiased Learning to Rank
Additive two-tower models are popular learning-to-rank methods for handling biased user feedback in industry settings. Recent studies, however, report a concerning phenomenon: training two-tower models on clicks collecte…
Learning-To-RankTowards Two-Stage Counterfactual Learning to Rank
Counterfactual learning to rank (CLTR) aims to learn a ranking policy from user interactions while correcting for the inherent biases in interaction data, such as position bias. Existing CLTR methods assume a single rank…
counterfactualLearning-To-RankLTRR: Learning To Rank Retrievers for LLMs
Retrieval-Augmented Generation (RAG) systems typically rely on a single fixed retriever, despite growing evidence that no single retriever performs optimally across all query types. In this paper, we explore a query rout…
Learning-To-RankRAGRetrieval-augmented GenerationPaths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery
Inferring causal relationships between variable pairs is crucial for understanding multivariate interactions in complex systems. Knowledge-based causal discovery -- which involves inferring causal relationships by reason…
Causal DiscoveryCausal InferenceKnowledge GraphsLearning-To-RankBounded-Abstention Pairwise Learning to Rank
Ranking systems influence decision-making in high-stakes domains like health, education, and employment, where they can have substantial economic and social impacts. This makes the integration of safety mechanisms essent…
Decision MakingLearning-To-RankLearning to Rank Chain-of-Thought: An Energy-Based Approach with Outcome Supervision
Mathematical reasoning presents a significant challenge for Large Language Models (LLMs), often requiring robust multi step logical consistency. While Chain of Thought (CoT) prompting elicits reasoning steps, it doesn't …
GSM8KLearning-To-RankMathMathematical ReasoningVisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to Rank
DeepSeek-R1 has demonstrated remarkable effectiveness in incentivizing reasoning and generalization capabilities of large language models (LLMs) through reinforcement learning. Nevertheless, the potential of reasoning-in…
Image GenerationImage Quality AssessmentLearning-To-RankNR-IQA+2Unlearning for Federated Online Learning to Rank: A Reproducibility Study
This paper reports on findings from a comparative study on the effectiveness and efficiency of federated unlearning strategies within Federated Online Learning to Rank (FOLTR), with specific attention to systematically a…
Learning-To-RankWho You Are Matters: Bridging Topics and Social Roles via LLM-Enhanced Logical Recommendation
Recommender systems filter contents/items valuable to users by inferring preferences from user features and historical behaviors. Mainstream approaches follow the learning-to-rank paradigm, which focus on discovering and…
General KnowledgeLarge Language ModelLearning-To-RankRecommendation Systems+2Beyond Pairwise Learning-To-Rank At Airbnb
There are three fundamental asks from a ranking algorithm: it should scale to handle a large number of items, sort items accurately by their utility, and impose a total order on the items for logical consistency. But her…
AllLearning-To-RankA Generative Re-ranking Model for List-level Multi-objective Optimization at Taobao
E-commerce recommendation systems aim to generate ordered lists of items for customers, optimizing multiple business objectives, such as clicks, conversions and Gross Merchandise Volume (GMV). Traditional multi-objective…
DiversityLearning-To-RankRecommendation Systemsregression+1Breaking Annotation Barriers: Generalized Video Quality Assessment via Ranking-based Self-Supervision
Video quality assessment (VQA) is essential for quantifying perceptual quality in various video processing workflows, spanning from camera capture systems to over-the-top streaming platforms. While recent supervised VQA …
Learning-To-RankSelf-Supervised LearningVideo Quality AssessmentVisual Question Answering (VQA)FAIR-QR: Enhancing Fairness-aware Information Retrieval through Query Refinement
Information retrieval systems such as open web search and recommendation systems are ubiquitous and significantly impact how people receive and consume online information. Previous research has shown the importance of fa…
FairnessInformation RetrievalLearning-To-RankRecommendation Systems+1HAPI: A Model for Learning Robot Facial Expressions from Human Preferences
Automatic robotic facial expression generation is crucial for human-robot interaction, as handcrafted methods based on fixed joint configurations often yield rigid and unnatural behaviors. Although recent automated techn…
Bayesian OptimizationFacial expression generationLearning-To-RankLong Context Modeling with Ranked Memory-Augmented Retrieval
Effective long-term memory management is crucial for language models handling extended contexts. We introduce a novel framework that dynamically ranks memory entries based on relevance. Unlike previous works, our model i…
Information RetrievalLearning-To-RankManagementRe-Ranking+1Weak Supervision for Improved Precision in Search Systems
Labeled datasets are essential for modern search engines, which increasingly rely on supervised learning methods like Learning to Rank and massive amounts of data to power deep learning models. However, creating these da…
Learning-To-RankEntailment-Preserving First-order Logic Representations in Natural Language Entailment
First-order logic (FOL) can represent the logical entailment semantics of natural language (NL) sentences, but determining natural language entailment using FOL remains a challenge. To address this, we propose the Entail…
DiversityLearning-To-RankUnbiased Learning to Rank with Query-Level Click Propensity Estimation: Beyond Pointwise Observation and Relevance
Most existing unbiased learning-to-rank (ULTR) approaches are based on the user examination hypothesis, which assumes that users will click a result only if it is both relevant and observed (typically modeled by position…
Learning-To-RankPositionImproving Similar Case Retrieval Ranking Performance By Revisiting RankSVM
Given the rapid development of Legal AI, a lot of attention has been paid to one of the most important legal AI tasks--similar case retrieval, especially with language models to use. In our paper, however, we try to impr…
Learning-To-RankRetrieval