recommender systems
AI solutions designed to predict user preferences and suggest relevant items or content, utilizing techniques like collaborative filtering or content-based filtering.
- Can LLMs Outshine Conventional Recommenders? A Comparative Evaluation
- Data-Free Model Extraction for Black-box Recommender Systems via Graph Convolutions
- Language Ranker: A Lightweight Ranking framework for LLM Decoding
- MultiScale Contextual Bandits for Long Term Objectives
- Negative Feedback Really Matters: Signed Dual-Channel Graph Contrastive Learning Framework for Recommendation
- ORBIT - Open Recommendation Benchmark for Reproducible Research with Hidden Tests
- Think before Recommendation: Autonomous Reasoning-enhanced Recommender
- Tightening Regret Lower and Upper Bounds in Restless Rising Bandits
- TranSUN: A Preemptive Paradigm to Eradicate Retransformation Bias Intrinsically from Regression Models in Recommender Systems
- True Impact of Cascade Length in Contextual Cascading Bandits
- Unveiling Extraneous Sampling Bias with Data Missing-Not-At-Random
- Who You Are Matters: Bridging Interests and Social Roles via LLM-Enhanced Logic Recommendation