Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
Explicit Modelling of Theory of Mind for Belief Prediction in Nonverbal Social Interactions
We propose MToMnet - a Theory of Mind (ToM) neural network for predicting beliefs and their dynamics during human social interactions from multimodal input. ToM is key for effective nonverbal human communication and coll…
Re-RankingDSLR: Document Refinement with Sentence-Level Re-ranking and Reconstruction to Enhance Retrieval-Augmented Generation
Recent advancements in Large Language Models (LLMs) have significantly improved their performance across various Natural Language Processing (NLP) tasks. However, LLMs still struggle with generating non-factual responses…
RAGRe-RankingRetrievalRetrieval-augmented Generation+1Efficient course recommendations with T5-based ranking and summarization
In this paper, we implement and evaluate a two-stage retrieval pipeline for a course recommender system that ranks courses for skill-occupation pairs. The in-production recommender system BrightFit provides course recomm…
In-Context LearningQuantizationRecommendation SystemsRe-Ranking+1Re-Ranking Step by Step: Investigating Pre-Filtering for Re-Ranking with Large Language Models
Large Language Models (LLMs) have been revolutionizing a myriad of natural language processing tasks with their diverse zero-shot capabilities. Indeed, existing work has shown that LLMs can be used to great effect for ma…
Information RetrievalPassage RankingPassage Re-RankingRe-RankingPerformative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems
Data bias, e.g., popularity impairs the dynamics of two-sided markets within recommender systems. This overshadows the less visible but potentially intriguing long-tail items that could capture user interest. Despite the…
FairnessRecommendation SystemsRe-RankingEfficient Document Ranking with Learnable Late Interactions
Cross-Encoder (CE) and Dual-Encoder (DE) models are two fundamental approaches for query-document relevance in information retrieval. To predict relevance, CE models use joint query-document embeddings, while DE models m…
Document RankingInformation RetrievalPassage Re-RankingRe-RankingDo Not Wait: Learning Re-Ranking Model Without User Feedback At Serving Time in E-Commerce
Recommender systems have been widely used in e-commerce, and re-ranking models are playing an increasingly significant role in the domain, which leverages the inter-item influence and determines the final recommendation …
Recommendation SystemsRe-RankingImproving Zero-shot LLM Re-Ranker with Risk Minimization
In the Retrieval-Augmented Generation (RAG) system, advanced Large Language Models (LLMs) have emerged as effective Query Likelihood Models (QLMs) in an unsupervised way, which re-rank documents based on the probability …
RAGRe-RankingRetrievalRetrieval-augmented GenerationGraph Neural Re-Ranking via Corpus Graph
Re-ranking systems aim to reorder an initial list of documents to satisfy better the information needs associated with a user-provided query. Modern re-rankers predominantly rely on neural network models, which have prov…
Re-RankingContextual Distillation Model for Diversified Recommendation
The diversity of recommendation is equally crucial as accuracy in improving user experience. Existing studies, e.g., Determinantal Point Process (DPP) and Maximal Marginal Relevance (MMR), employ a greedy paradigm to ite…
DiversityKnowledge DistillationmodelRe-RankingA Bi-metric Framework for Fast Similarity Search
We propose a new "bi-metric" framework for designing nearest neighbor data structures. Our framework assumes two dissimilarity functions: a ground-truth metric that is accurate but expensive to compute, and a proxy metri…
MTEB BenchmarkRe-RankingText RetrievalCluster-Aware Similarity Diffusion for Instance Retrieval
Diffusion-based re-ranking is a common method used for retrieving instances by performing similarity propagation in a nearest neighbor graph. However, existing techniques that construct the affinity graph based on pairwi…
MisinformationRe-RankingRetrievalKeyword-driven Retrieval-Augmented Large Language Models for Cold-start User Recommendations
Recent advancements in Large Language Models (LLMs) have shown significant potential in enhancing recommender systems. However, addressing the cold-start recommendation problem, where users lack historical data, remains …
Recommendation SystemsRe-RankingRetrievalMulti-stage Retrieve and Re-rank Model for Automatic Medical Coding Recommendation
The International Classification of Diseases (ICD) serves as a definitive medical classification system encompassing a wide range of diseases and conditions. The primary objective of ICD indexing is to allocate a subset …
Contrastive LearningRe-RankingRetrievalEffoVPR: Effective Foundation Model Utilization for Visual Place Recognition
The task of Visual Place Recognition (VPR) is to predict the location of a query image from a database of geo-tagged images. Recent studies in VPR have highlighted the significant advantage of employing pre-trained found…
Re-RankingVisual Place RecognitionAdapNet: Adaptive Noise-Based Network for Low-Quality Image Retrieval
Image retrieval aims to identify visually similar images within a database using a given query image. Traditional methods typically employ both global and local features extracted from images for matching, and may also a…
Image RetrievalRe-RankingRetrievalPredicting from a Different Perspective: A Re-ranking Model for Inductive Knowledge Graph Completion
Rule-induction models have demonstrated great power in the inductive setting of knowledge graph completion. In this setting, the models are tested on a knowledge graph entirely composed of unseen entities. These models l…
Inductive knowledge graph completionKnowledge Graph CompletionLink PredictionRe-RankingGuaranteeing Accuracy and Fairness under Fluctuating User Traffic: A Bankruptcy-Inspired Re-ranking Approach
Out of sustainable and economical considerations, two-sided recommendation platforms must satisfy the needs of both users and providers. Previous studies often show that the two sides' needs show different urgency: provi…
FairnessRecommendation SystemsRe-RankingA Preference-oriented Diversity Model Based on Mutual-information in Re-ranking for E-commerce Search
Re-ranking is a process of rearranging ranking list to more effectively meet user demands by accounting for the interrelationships between items. Existing methods predominantly enhance the precision of search results, of…
DiversityRe-RankingVariational InferenceTop-Down Partitioning for Efficient List-Wise Ranking
Large Language Models (LLMs) have significantly impacted many facets of natural language processing and information retrieval. Unlike previous encoder-based approaches, the enlarged context window of these generative mod…
Information RetrievalRe-Ranking