Awesome Two-Tower & Retrieval

Key papers

• MTGR: Industrial-Scale Generative Recommendation Framework in Meituan (2025)
  Ruidong Han et al.
  11.46
• Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders (2024)
  Yupeng Hou et al.
  11.11
• Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video Recommendation (2025)
  Han Liu et al.
  9.87
• PLUM: Adapting Pre-trained Language Models for Industrial-scale Generative Recommendations (2025)
  Ruining He et al.
  8.78
• DAS: Dual-Aligned Semantic IDs Empowered Industrial Recommender System (2025)
  Wencai Ye et al.
  7.97
• Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation (2025)
  Shijie Wang et al.
  7.84
• 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
• Large-scale Benchmarks for Multimodal Recommendation with Ducho (2024)
  Matteo Attimonelli 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
• Graph Retrieval-Augmented LLM for Conversational Recommendation Systems (2025)
  Zhangchi Qiu et al.
  7.07
• RecoWorld: Building Simulated Environments for Agentic Recommender Systems (2025)
  Fei Liu et al.
  6.56
• Progressive Semantic Residual Quantization for Multimodal-Joint Interest Modeling in Music Recommendation (2025)
  Shijia Wang et al.
  6.51
• Semantic Retrieval Augmented Contrastive Learning for Sequential Recommendation (2025)
  Ziqiang Cui et al.
  6.23
• HiD-VAE: Interpretable Generative Recommendation via Hierarchical and Disentangled Semantic IDs (2025)
  Dengzhao Fang et al.
  6.23
• Just Ask for Music (JAM): Multimodal and Personalized Natural Language Music Recommendation (2025)
  Alessandro B. Melchiorre et al.
  6.18
• Rethinking Contrastive Learning in Session-based Recommendation (2025)
  Xiaokun Zhang et al.
  6.12
• Bi-Level Optimization for Generative Recommendation: Bridging Tokenization and Generation (2025)
  Yimeng Bai et al.
  6.04
• Pctx: Tokenizing Personalized Context for Generative Recommendation (2025)
  Qiyong Zhong et al.
  6.04
• 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
• MR.Rec: Synergizing Memory and Reasoning for Personalized Recommendation Assistant with LLMs (2025)
  Jiani Huang et al.
  5.68
• Empowering Retrieval-based Conversational Recommendation with Contrasting User Preferences (2025)
  Heejin Kook et al.
  5.65
• Semantic IDs for Music Recommendation (2025)
  M. Jeffrey Mei et al.
  5.52
• Bridging the Semantic-Collaborative Gap: An Asymmetric Graph Architecture for Cold-Start Item Recommendation (2026)
  Anh Truong et al.
  5.49
• Benchmarking LLM Agents on Meta-Analysis Articles from Nature Portfolio (2026)
  Anzhe Xie et al.
  5.49
• Beyond Parallel Sampling: Diverse Query Initialization for Agentic Search (2026)
  Sidhaarth Murali et al.
  5.49
• CORONA: A Coarse-to-Fine Framework for Graph-based Recommendation with Large Language Models (2025)
  Junze Chen et al.
  5.46
• Retrieval Augmented Generation with Collaborative Filtering for Personalized Text Generation (2025)
  Teng Shi et al.
  5.35
• HyMiRec: A Hybrid Multi-interest Learning Framework for LLM-based Sequential Recommendation (2025)
  Jingyi Zhou et al.
  5.26
• 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
• A Hybrid Cross-Stage Coordination Pre-ranking Model for Online Recommendation Systems (2025)
  Binglei Zhao et al.
  5.24
• Denoising Neural Reranker for Recommender Systems (2025)
  Wenyu Mao 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
• Bootstrapping Conditional Retrieval for User-to-Item Recommendations (2025)
  Hongtao Lin et al.
  5.15
• TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback (2022)
  Jie Wang et al.
  5.06
• Optimizing Recall or Relevance? A Multi-Task Multi-Head Approach for Item-to-Item Retrieval in Recommendation (2025)
  Jiang Zhang et al.
  5.04
• 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
• Non-negative Elastic Net Decoding for Information Retrieval (2026)
  Koki Okajima et al.
  5.01
• DeGRe: Dense-supervised Generative Reranking for Recommendation (2026)
  Chaotian Song et al.
  4.95
• L2Rec: Towards Dual-View Understanding of LLMs for Personalized Recommendation (2026)
  Pingjun Pan et al.
  4.95
• OmniSage: Large Scale, Multi-Entity Heterogeneous Graph Representation Learning (2025)
  Anirudhan Badrinath et al.
  4.93
• TutorLLM: Customizing Learning Recommendations with Knowledge Tracing and Retrieval-Augmented Generation (2025)
  Zhaoxing Li et al.
  4.82
• GRank: Towards Target-Aware and Streamlined Industrial Retrieval with a Generate-Rank Framework (2025)
  Yijia Sun et al.
  4.75
• Towards Context-aware Reasoning-enhanced Generative Searching in E-commerce (2025)
  Zhiding Liu et al.
  4.75
• Massive Memorization with Hundreds of Trillions of Parameters for Sequential Transducer Generative Recommenders (2025)
  Zhimin Chen et al.
  4.75
• Item-Language Model for Conversational Recommendation (2024)
  Li Yang et al.
  4.68
• Refining Contrastive Learning and Homography Relations for Multi-Modal Recommendation (2025)
  Shouxing Ma et al.
  4.64
• MISS: Multi-Modal Tree Indexing and Searching with Lifelong Sequential Behavior for Retrieval Recommendation (2025)
  Chengcheng Guo et al.
  4.64
• Modeling Long-term User Behaviors with Diffusion-driven Multi-interest Network for CTR Prediction (2025)
  Weijiang Lai et al.
  4.64
• Efficient Multi-Task Learning via Generalist Recommender (2025)
  Luyang Wang et al.
  4.42
• RALLRec+: Retrieval Augmented Large Language Model Recommendation with Reasoning (2025)
  Sichun Luo et al.
  4.36
• RAGEAR: Retrieval-Augmented Graph-Enhanced Academic Recommender (2026)
  Francesco Granata et al.
  4.33
• Intent Representation Learning with Large Language Model for Recommendation (2025)
  Yu Wang and Lei Sang and Yi Zhang and Yiwen Zhang
  4.30
• Collaborative Retrieval for Large Language Model-based Conversational Recommender Systems (2025)
  Yaochen Zhu et al.
  4.30
• MixRec: Individual and Collective Mixing Empowers Data Augmentation for Recommender Systems (2025)
  Yi Zhang and Yiwen Zhang
  4.25