Papers Learning-To-Rank
“Learning-To-Rank” 태그가 달린 논문 753편 · 필터 해제
Cascading Bandits Robust to Adversarial Corruptions
Online learning to rank sequentially recommends a small list of items to users from a large candidate set and receives the users' click feedback. In many real-world scenarios, users browse the recommended list in order a…
Learning-To-RankOffline Learning for Combinatorial Multi-armed Bandits
The combinatorial multi-armed bandit (CMAB) is a fundamental sequential decision-making framework, extensively studied over the past decade. However, existing work primarily focuses on the online setting, overlooking the…
Decision MakingLanguage ModelingLanguage ModellingLarge Language Model+3Reqo: A Robust and Explainable Query Optimization Cost Model
In recent years, there has been a growing interest in using machine learning (ML) in query optimization to select more efficient plans. Existing learning-based query optimizers use certain model architectures to convert …
Learning-To-RankRAMQA: A Unified Framework for Retrieval-Augmented Multi-Modal Question Answering
Multi-modal retrieval-augmented Question Answering (MRAQA), integrating text and images, has gained significant attention in information retrieval (IR) and natural language processing (NLP). Traditional ranking methods r…
DecoderInformation RetrievalLearning-To-RankMulti-Task Learning+3Learning to Rank Aspects and Opinions for Comparative Explanations
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. This work extends the notion of compar…
Explainable RecommendationLearning-To-RankEfficient support ticket resolution using Knowledge Graphs
A review of over 160,000 customer cases indicates that about 90% of time is spent by the product support for solving around 10% of subset of tickets where a trivial solution may not exist. Many of these challenging cases…
Knowledge Graph EmbeddingsKnowledge GraphsLearning-To-RankLearning to Rank Pre-trained Vision-Language Models for Downstream Tasks
Vision language models (VLMs) like CLIP show stellar zero-shot capability on classification benchmarks. However, selecting the VLM with the highest performance on the unlabeled downstream task is non-trivial. Existing VL…
Learning-To-RankModel SelectionEffective and secure federated online learning to rank
Online Learning to Rank (OLTR) optimises ranking models using implicit user feedback, such as clicks. Unlike traditional Learning to Rank (LTR) methods that rely on a static set of training data with relevance judgements…
Federated LearningLearning-To-RankRanking-aware adapter for text-driven image ordering with CLIP
Recent advances in vision-language models (VLMs) have made significant progress in downstream tasks that require quantitative concepts such as facial age estimation and image quality assessment, enabling VLMs to explore …
Age EstimationImage Quality AssessmentLearning-To-RankLearning Cluster Representatives for Approximate Nearest Neighbor Search
Developing increasingly efficient and accurate algorithms for approximate nearest neighbor search is a paramount goal in modern information retrieval. A primary approach to addressing this question is clustering, which i…
ClusteringInformation RetrievalLearning-To-RankGABAR: Graph Attention-Based Action Ranking for Relational Policy Learning
We propose a novel approach to learn relational policies for classical planning based on learning to rank actions. We introduce a new graph representation that explicitly captures action information and propose a Graph N…
Graph AttentionGraph Neural NetworkLearning-To-RankA Versatile Influence Function for Data Attribution with Non-Decomposable Loss
Influence function, a technique rooted in robust statistics, has been adapted in modern machine learning for a novel application: data attribution -- quantifying how individual training data points affect a model's predi…
Information RetrievalLearning-To-RankSurvival AnalysisICLERB: In-Context Learning Embedding and Reranker Benchmark
In-Context Learning (ICL) enables Large Language Models (LLMs) to perform new tasks by conditioning on prompts with relevant information. Retrieval-Augmented Generation (RAG) enhances ICL by incorporating retrieved docum…
In-Context LearningInformation RetrievalLearning-To-RankRAG+2LEADRE: Multi-Faceted Knowledge Enhanced LLM Empowered Display Advertisement Recommender System
Display advertising provides significant value to advertisers, publishers, and users. Traditional display advertising systems utilize a multi-stage architecture consisting of retrieval, coarse ranking, and final ranking.…
Learning-To-RankPrompt EngineeringRecommendation SystemsRetrieval+1A Survey on E-Commerce Learning to Rank
In e-commerce, ranking the search results based on users' preference is the most important task. Commercial e-commerce platforms, such as, Amazon, Alibaba, eBay, Walmart, etc. perform extensive and relentless research to…
Learning-To-RankSurveyTSPRank: Bridging Pairwise and Listwise Methods with a Bilinear Travelling Salesman Model
Traditional Learning-To-Rank (LETOR) approaches, including pairwise methods like RankNet and LambdaMART, often fall short by solely focusing on pairwise comparisons, leading to sub-optimal global rankings. Conversely, de…
Information RetrievalLearning-To-RankAn Early FIRST Reproduction and Improvements to Single-Token Decoding for Fast Listwise Reranking
Recent advances have demonstrated that large language models (LLMs) excel as listwise rerankers, but their high computational demands remain a barrier to widespread adoption. Further, the traditional language modeling (L…
Language ModelingLanguage ModellingLearning-To-RankRerankingLearning to Rank Salient Content for Query-focused Summarization
This study examines the potential of integrating Learning-to-Rank (LTR) with Query-focused Summarization (QFS) to enhance the summary relevance via content prioritization. Using a shared secondary decoder with the summar…
DecoderLearning-To-RankQuery-focused SummarizationDon't Just Pay Attention, PLANT It: Transfer L2R Models to Fine-tune Attention in Extreme Multi-Label Text Classification
State-of-the-art Extreme Multi-Label Text Classification (XMTC) models rely heavily on multi-label attention layers to focus on key tokens in input text, but obtaining optimal attention weights is challenging and resourc…
DecoderLearning-To-RankMulti Label Text ClassificationMulti-Label Text Classification+3Combinatorial Logistic Bandits
We introduce a novel framework called combinatorial logistic bandits (CLogB), where in each round, a subset of base arms (called the super arm) is selected, with the outcome of each base arm being binary and its expectat…
Computational EfficiencyLearning-To-Rank