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Learning-To-Rank

Learning to rank is the application of machine learning to build ranking models. Some common use cases for ranking models are information retrieval (e.g., web search) and news feeds application (think Twitter, Facebook, Instagram).

Papers

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Showing 376–400 of 753 papers

TitleStatusHype
Learning to rank music tracks using triplet loss—0
Learning to Rank Normalized Entropy Curves with Differentiable Window Transformation—0
Learning to Rank Onset-Occurring-Offset Representations for Micro-Expression Recognition—0
VSoLSCSum: Building a Vietnamese Sentence-Comment Dataset for Social Context Summarization—0
Learning to Rank Personalized Search Results in Professional Networks—0
Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks—0
Learning to Rank Proposals for Object Detection—0
Learning to rank quantum circuits for hardware-optimized performance enhancement—0
Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems—0
Answering questions by learning to rank - Learning to rank by answering questions—0
TopRank: A practical algorithm for online stochastic ranking—0
Learning to Rank Question Answer Pairs with Bilateral Contrastive Data Augmentation—0
TopRank+: A Refinement of TopRank Algorithm—0
Learning To Rank Resources with GNN—0
Learning to Rank Retargeted Images—0
Learning to Rank Salient Content for Query-focused Summarization—0
Learning to Rank Scientific Documents from the Crowd—0
Learning to Rank Semantic Coherence for Topic Segmentation—0
Addressing Purchase-Impression Gap through a Sequential Re-ranker—0
Learning to Rank under Multinomial Logit Choice—0
Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval—0
Learning to Rank Utterances for Query-Focused Meeting Summarization—0
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction—0
Learning to Rank Visual Stories From Human Ranking Data—0
Towards Better Web Search Performance: Pre-training, Fine-tuning and Learning to Rank—0
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