Research map / Label-efficient and robust learning / Recognition and long-tail
Who Likes What? — SplitLBI in Exploring Preferential Diversity of Ratings
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Tree of Preferences for Diversified Recommendation (NeurIPS 2025 · similarity 0.87)
- Comparing Few to Rank Many: Active Human Preference Learning Using Randomized Frank-Wolfe Method (ICML 2025 · similarity 0.85)
- Preference Learning Algorithms Do Not Learn Preference Rankings (NeurIPS 2024 · similarity 0.85)
- Preference learning made easy: Everything should be understood through win rate (ICML 2025 · similarity 0.85)
- No Preference Left Behind: Group Distributional Preference Optimization (ICLR 2025 · similarity 0.85)
- Preference Diffusion for Recommendation (ICLR 2025 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.