arXiv:2409.13703
Zeroshot Listwise Learning to Rank Algorithm for Recommendation
원문 초록
Learning to rank is a rare technology compared with other techniques such as deep neural networks. The number of experts in the field is roughly 1/6 of the number of professionals in deep learning. Being an effective ranking methodology, learning to rank has been widely used in the field of information retrieval. However, in recent years, learning to rank as a recommendation approach has been on decline. In this paper, we take full advantage of order statistic approximation and power law distribution to design a zeroshot listwise learning to rank algorithm for recommendation. We prove in the experiment section that our approach is both accurate and fair.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
JointRank: Rank Large Set with Single Pass
From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents
DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents
Exp4Fuse: A Rank Fusion Framework for Enhanced Sparse Retrieval using Large Language Model-based Query Expansion
Retrieval Augmented Generation based Large Language Models for Causality Mining
From Chaos to Automation: Enabling the Use of Unstructured Data for Robotic Process Automation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.