Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
Generator and Critic: A Deep Reinforcement Learning Approach for Slate Re-ranking in E-commerce
The slate re-ranking problem considers the mutual influences between items to improve user satisfaction in e-commerce, compared with the point-wise ranking. Previous works either directly rank items by an end to end mode…
Deep Reinforcement LearningDiversityreinforcement-learningReinforcement Learning+2Opportunistic Multi-aspect Fairness through Personalized Re-ranking
As recommender systems have become more widespread and moved into areas with greater social impact, such as employment and housing, researchers have begun to seek ways to ensure fairness in the results that such systems …
AttributeFairnessRecommendation SystemsRe-RankingAttribute-guided Feature Extraction and Augmentation Robust Learning for Vehicle Re-identification
Vehicle re-identification is one of the core technologies of intelligent transportation systems and smart cities, but large intra-class diversity and inter-class similarity poses great challenges for existing method. In …
AttributeDiversityRe-RankingVehicle Re-IdentificationGraph-Embedding Empowered Entity Retrieval
In this research, we improve upon the current state of the art in entity retrieval by re-ranking the result list using graph embeddings. The paper shows that graph embeddings are useful for entity-oriented search tasks. …
Entity RetrievalGraph EmbeddingRe-RankingRetrieval+1Automatic Reconstruction of Missing Romanian Cognates and Unattested Latin Words
Producing related words is a key concern in historical linguistics. Given an input word, the task is to automatically produce either its proto-word, a cognate pair or a modern word derived from it. In this paper, we appl…
Re-RankingParaphrasing vs Coreferring: Two Sides of the Same Coin
We study the potential synergy between two different NLP tasks, both confronting predicate lexical variability: identifying predicate paraphrases, and event coreference resolution. First, we used annotations from an even…
coreference-resolutionCoreference ResolutionEvent Coreference ResolutionEvent Cross-Document Coreference Resolution+2Training Curricula for Open Domain Answer Re-Ranking
In precision-oriented tasks like answer ranking, it is more important to rank many relevant answers highly than to retrieve all relevant answers. It follows that a good ranking strategy would be to learn how to identify …
Re-RankingEfficient Document Re-Ranking for Transformers by Precomputing Term Representations
Deep pretrained transformer networks are effective at various ranking tasks, such as question answering and ad-hoc document ranking. However, their computational expenses deem them cost-prohibitive in practice. Our propo…
Document RankingQuestion AnsweringRe-RankingTopic Propagation in Conversational Search
In a conversational context, a user expresses her multi-faceted information need as a sequence of natural-language questions, i.e., utterances. Starting from a given topic, the conversation evolves through user utterance…
Conversational SearchRe-RankingRetrievalColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
Recent progress in Natural Language Understanding (NLU) is driving fast-paced advances in Information Retrieval (IR), largely owed to fine-tuning deep language models (LMs) for document ranking. While remarkably effectiv…
Document RankingInformation RetrievalNatural Language UnderstandingRe-Ranking+1Multi-Domain Learning and Identity Mining for Vehicle Re-Identification
This paper introduces our solution for the Track2 in AI City Challenge 2020 (AICITY20). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synthetic data. Our solution is based on a s…
ClusteringRe-RankingVehicle Re-IdentificationBeyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation
With the uptake of algorithmic personalization in the news domain, news organizations increasingly trust automated systems with previously considered editorial responsibilities, e.g., prioritizing news to readers. In thi…
ArticlesDiversityNews RecommendationRecommendation Systems+1Dual Embedding Expansion for Vehicle Re-identification
Vehicle re-identification plays a crucial role in the management of transportation infrastructure and traffic flow. However, this is a challenging task due to the large view-point variations in appearance, environmental …
AttributeImage RetrievalManagementRe-Ranking+2Learning-to-Rank with BERT in TF-Ranking
This paper describes a machine learning algorithm for document (re)ranking, in which queries and documents are firstly encoded using BERT [1], and on top of that a learning-to-rank (LTR) model constructed with TF-Ranking…
Document RankingLearning-To-RankPassage Re-RankingRe-RankingPersonalized Re-ranking for Improving Diversity in Live Recommender Systems
Users of industrial recommender systems are normally suggesteda list of items at one time. Ideally, such list-wise recommendationshould provide diverse and relevant options to the users. However, in practice, list-wise r…
DiversityRecommendation SystemsRe-RankingTwo-stage Discriminative Re-ranking for Large-scale Landmark Retrieval
We propose an efficient pipeline for large-scale landmark image retrieval that addresses the diversity of the dataset through two-stage discriminative re-ranking. Our approach is based on embedding the images in a featur…
DiversityImage RetrievalLandmark RecognitionRe-Ranking+2Comment Ranking Diversification in Forum Discussions
Viewing consumption of discussion forums with hundreds or more comments depends on ranking because most users only view top-ranked comments. When comments are ranked by an ordered score (e.g. number of replies or up-vote…
Re-RankingSemantic SimilaritySemantic Textual SimilarityDifferentiable Reasoning over a Virtual Knowledge Base
We consider the task of answering complex multi-hop questions using a corpus as a virtual knowledge base (KB). In particular, we describe a neural module, DrKIT, that traverses textual data like a KB, softly following pa…
Re-RankingInterpretable & Time-Budget-Constrained Contextualization for Re-Ranking
Search engines operate under a strict time constraint as a fast response is paramount to user satisfaction. Thus, neural re-ranking models have a limited time-budget to re-rank documents. Given the same amount of time, a…
Re-RankingWord EmbeddingsDeep Graph Matching Consensus
This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs. First, we use localized node embeddings computed by a graph neural network to obtain an initial rank…
Entity AlignmentGraph MatchingGraph Neural NetworkKnowledge Graphs+1