Awesome Ranking & CTR

Key papers

• MTGR: Industrial-Scale Generative Recommendation Framework in Meituan (2025)
  Ruidong Han et al.
  11.46
• Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video Recommendation (2025)
  Han Liu et al.
  9.87
• EAGER-LLM: Enhancing Large Language Models as Recommenders through Exogenous Behavior-Semantic Integration (2025)
  Minjie Hong et al.
  9.09
• Collaboration of Large Language Models and Small Recommendation Models for Device-Cloud Recommendation (2025)
  Zheqi Lv et al.
  8.81
• PLUM: Adapting Pre-trained Language Models for Industrial-scale Generative Recommendations (2025)
  Ruining He et al.
  8.78
• RLHF Fine-Tuning of LLMs for Alignment with Implicit User Feedback in Conversational Recommenders (2025)
  Zhongheng Yang et al.
  8.10
• MMQ: Multimodal Mixture-of-Quantization Tokenization for Semantic ID Generation and User Behavioral Adaptation (2025)
  Yi Xu et al.
  7.68
• Semantic IDs for Joint Generative Search and Recommendation (2025)
  Gustavo Penha et al.
  7.52
• Deep Pareto Reinforcement Learning for Multi-Objective Recommender Systems (2024)
  Pan Li et al.
  7.50
• Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions (2023)
  Timo Wilm et al.
  7.38
• MiniOneRec: An Open-Source Framework for Scaling Generative Recommendation (2025)
  Xiaoyu Kong et al.
  7.28
• Large-scale Benchmarks for Multimodal Recommendation with Ducho (2024)
  Matteo Attimonelli et al.
  7.17
• Breaking the Top-$K$ Barrier: Advancing Top-$K$ Ranking Metrics Optimization in Recommender Systems (2025)
  Weiqin Yang et al.
  7.17
• Taming the One-Epoch Phenomenon in Online Recommendation System by Two-stage Contrastive ID Pre-training (2025)
  Yi-Ping Hsu et al.
  7.17
• Category-based and Popularity-guided Video Game Recommendation: A Balance-oriented Framework (2026)
  Xiping Li et al.
  7.13
• LettinGo: Explore User Profile Generation for Recommendation System (2025)
  Lu Wang et al.
  7.06
• NLGR: Utilizing Neighbor Lists for Generative Rerank in Personalized Recommendation Systems (2025)
  Shuli Wang et al.
  7.02
• ThinkRec: Thinking-based recommendation via LLM (2025)
  Qihang Yu et al.
  7.00
• Reinforced Prompt Personalization for Recommendation with Large Language Models (2024)
  Wenyu Mao et al.
  6.96
• Comprehensive List Generation for Multi-Generator Reranking (2025)
  Hailan Yang et al.
  6.95
• Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Pair Selection (2025)
  Zhikai Wang et al.
  6.53
• TrackRec: Iterative Alternating Feedback with Chain-of-Thought via Preference Alignment for Recommendation (2025)
  Yu Xia et al.
  6.51
• Progressive Semantic Residual Quantization for Multimodal-Joint Interest Modeling in Music Recommendation (2025)
  Shijia Wang et al.
  6.51
• Scaling Transformers for Discriminative Recommendation via Generative Pretraining (2025)
  Chunqi Wang et al.
  6.39
• Multi-Behavior Recommender Systems: A Survey (2025)
  Kyungho Kim et al.
  6.23
• HiD-VAE: Interpretable Generative Recommendation via Hierarchical and Disentangled Semantic IDs (2025)
  Dengzhao Fang et al.
  6.23
• Tricolore: Multi-Behavior User Profiling for Enhanced Candidate Generation in Recommender Systems (2025)
  Xiao Zhou et al.
  6.07
• Hierarchical Tree Search-based User Lifelong Behavior Modeling on Large Language Model (2025)
  Yu Xia et al.
  6.07
• Pctx: Tokenizing Personalized Context for Generative Recommendation (2025)
  Qiyong Zhong et al.
  6.04
• Rankformer: A Graph Transformer for Recommendation based on Ranking Objective (2025)
  Sirui Chen et al.
  5.96
• ESANS: Effective and Semantic-Aware Negative Sampling for Large-Scale Retrieval Systems (2025)
  Haibo Xing et al.
  5.90
• Unleashing the Potential of Two-Tower Models: Diffusion-Based Cross-Interaction for Large-Scale Matching (2025)
  Yihan Wang et al.
  5.90
• Diffusion Generative Recommendation with Continuous Tokens (2025)
  Haohao Qu et al.
  5.70
• Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration (2025)
  Hongji Li et al.
  5.70
• Rank-GRPO: Training LLM-based Conversational Recommender Systems with Reinforcement Learning (2025)
  Yaochen Zhu et al.
  5.68
• Empowering Retrieval-based Conversational Recommendation with Contrasting User Preferences (2025)
  Heejin Kook et al.
  5.65
• Evaluating Podcast Recommendations with Profile-Aware LLM-as-a-Judge (2025)
  Francesco Fabbri et al.
  5.57
• Large Language Model driven Policy Exploration for Recommender Systems (2025)
  Jie Wang et al.
  5.54
• Semantic IDs for Music Recommendation (2025)
  M. Jeffrey Mei et al.
  5.52
• A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges (2025)
  Rahul Raja et al.
  5.52
• Benchmarking LLM Agents on Meta-Analysis Articles from Nature Portfolio (2026)
  Anzhe Xie et al.
  5.49
• Field Matters: A Lightweight LLM-enhanced Method for CTR Prediction (2025)
  Yu Cui et al.
  5.40
• Don't Waste It: Guiding Generative Recommenders with Structured Human Priors via Multi-Head Decoding (2025)
  Yunkai Zhang et al.
  5.32
• Multimodal Item Scoring for Natural Language Recommendation via Gaussian Process Regression with LLM Relevance Judgments (2025)
  Yifan Liu et al.
  5.26
• RALLRec: Improving Retrieval Augmented Large Language Model Recommendation with Representation Learning (2025)
  Jian Xu et al.
  5.24
• Beyond Explicit and Implicit: How Users Provide Feedback to Shape Personalized Recommendation Content (2025)
  Wenqi Li et al.
  5.24
• A Hybrid Cross-Stage Coordination Pre-ranking Model for Online Recommendation Systems (2025)
  Binglei Zhao et al.
  5.24
• Generative Large Recommendation Models: Emerging Trends in LLMs for Recommendation (2025)
  Hao Wang and Wei Guo and Luankang Zhang and Jin Yao Chin and Yufei Ye and Huifeng Guo and Yong Liu and Defu Lian and Ruiming Tang and Enhong Chen
  5.24
• Denoising Neural Reranker for Recommender Systems (2025)
  Wenyu Mao et al.
  5.21
• Interactive Recommendation Agent with Active User Commands (2025)
  Jiakai Tang et al.
  5.21
• Request-Only Optimization for Recommendation Systems (2025)
  Liang Guo et al.
  5.15
• FuXi-\beta: Towards a Lightweight and Fast Large-Scale Generative Recommendation Model (2025)
  Yufei Ye et al.
  5.15
• You Only Evaluate Once: A Tree-based Rerank Method at Meituan (2025)
  Shuli Wang et al.
  5.15
• Bootstrapping Conditional Retrieval for User-to-Item Recommendations (2025)
  Hongtao Lin et al.
  5.15
• Listwise Preference Alignment Optimization for Tail Item Recommendation (2025)
  Zihao Li et al.
  5.10
• Optimizing Recall or Relevance? A Multi-Task Multi-Head Approach for Item-to-Item Retrieval in Recommendation (2025)
  Jiang Zhang et al.
  5.04
• Designing Recommendation Exposure and Favorite Lists: A Field Experiment in a Spot-Work Platform (2026)
  Kazuki Sekiya et al.
  5.01
• RSRank: Learning Relevance from Representational Shifts (2026)
  Archit Gupta et al.
  5.01
• Temporal Preference Optimization for Unsupervised Retrieval (2026)
  HyunJin Kim et al.
  5.01
• Understanding and Debugging Failures in N-Gram-Based Generative Retrieval (2026)
  Richard Takacs et al.
  5.01