Real-World Content Recommendation Problem

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7 papers in the last four weeks, up 133% on the four weeks before. 0.1% of all new papers.

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Latest papers 38

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Beyond Interaction Capacity: Estimator Scaling with Recursive Models for CTR Prediction
Sep 29, 2026Shivang Chopra, Fotis Iliopoulos, Zsolt Kira +1Click-Through Rate Prediction Real-World Content Recommendation Problem
Georgia Institute of Technology · Google Research
FairDiff: Mitigating the Self-Reinforcing Matthew Effect in Diffusion Recommender Models
Sep 29, 2026Song-Li Wu, Xianquan Wang, Zhaocheng Du +2Real-World Content Recommendation Problem Diffusion Models
Tsinghua University · University of Science and Technology of China · Huawei Noah’s Ark Lab
Eval4DiRec: A Unified and Systematic Evaluation Framework for Diffusion-based Recommender Systems
Sep 28, 2026Cong Wang, Shoujin Wang, Yishuo Li +3Real-World Content Recommendation Problem Evaluation Benchmarks
Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences); Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science, Jinan, Shandong, China · University of Technology Sydney, Sydney, NSW, Australia · Department of Computer Science and Technology, Tongji University, Shanghai, China +1
A Systematic Benchmark of Explainable Methods for Temporal Attribution in Sequential Recommendation Systems
Sep 23, 2026Akash Pandey, Kanisha Shah, Addrish Roy +5Multimodal Recommendation Model Sequential Recommendation
Capital One, AI Foundations San Jose, CA, USA · Capital One, AI Foundations New York, NY, USA · Capital One, AI Foundations McLean, VA, USA +1
Lightweight Ranking Heads: Accelerating Multi-Task Experimentation in Production Recommender Systems
Sep 21, 2026Sanjay Surendranath Girija, Aniruddh Nath, Li Wei +6Real-World Content Recommendation Problem Ranking
Google LLC Mountain View, CA, USA
Self-Evolving Memory for Generative Recommendation
Sep 14, 2026Xinyu Lin, Zhuosong Jiang, Zixiao Suo +12Real-World Content Recommendation Problem Recommendation
National University of Singapore · Meta AI
On the Regularization Landscape for the Linear Recommendation Models
Sep 12, 2026Dong Li, Zhenming Liu, Ruoming Jin +4Real-World Content Recommendation Problem Low-Rank Structure
Kent State University, USA · College of William and Mary, USA · iLambda, USA
EPIC: Explicit Posterior Item Conditioning for Semantic ID Diffusion Recommendation
Sep 3, 2026Tuan-Binh Tran, Thanh Tam Nguyen, Quoc Viet Hung Nguyen +3Real-World Content Recommendation Problem Recommendation
College of Engineering and Computer Science VinUniversity Hanoi, Vietnam · School of Information and Communication Technology Griffith University Gold Coast, Queensland, Australia · Department of Computer Science Aalborg University Copenhagen, Denmark
Batch Size or Negatives? A Selection Rule for Memory-Constrained Recommender Training
Aug 11, 2026Artyom Sabitov, Daniil Volkov, Alexey ZaytsevReal-World Content Recommendation Problem Bounded-Memory
Applied AI Institute, Moscow, Russia Risk department
Adaptive Semantic Capacity Allocation for Parallel Generative Recommendation
Aug 10, 2026Chenxi Li, Yuchen Lu, Xu YangSequential Recommendation Real-World Content Recommendation Problem
University of the Chinese Academy of Sciences · Institute of Automation, Chinese Academy of Sciences
Heterogeneous Ranking in Industrial-Scale Recommender Systems: A Case Study
Jul 30, 2026Di Bai, Jintao Liu, Zhenwei Tang +3Real-World Content Recommendation Problem Heterogeneity
Google LLC Mountain View, CA, USA · Google LLC Toronto, ON, Canada · Google LLC New York, NY, USA +1
OneShot: Index-in-Ranking with Neural Scoring for Large-Scale Retrieval
Jul 29, 2026Ziwei Li, Shuyao Li, Xufeng Cai +12Real-World Content Recommendation Problem Retrievers
Meta Platforms, Inc. ∗USA, †Singapore
TRWH: A Text-Driven Random Walk Heterogeneous GNN for Semantic-Aware Sparse Recommendation
Jul 28, 2026He Ma, Chen LiuReal-World Content Recommendation Problem Graph Neural Networks
The University of Sydney Camperdown, NSW, 2006, Australia · Nankai University Tianjing, China
TopoGR: Revealing and Preserving Latent Structure of Semantic ID in Generative Recommendation
Jul 28, 2026Ziyu Zheng, Zhengshun Du, Yaming Yang +5Real-World Content Recommendation Problem Semantic Relationships
Xidian University, Xi’an, China · Alibaba Beijing, China · University of Science and Technology of China Hefei, China
Probabilistic Residual Learning for Online Recommendations
Jul 23, 2026Wenyuan Wang, Yusong Zhao, Zihao Xu +11Real-World Content Recommendation Problem Residual Learning
Rutgers University · Piscataway, New Jersey, United States · Meta +4
Cardinality-Decomposed Loss: Matching Training Objectives to Relation Structure in Heterogeneous Recommendation Graphs
Jul 22, 2026Parul Maheshwari, Amulya Paruchuri, Yiqing Zou +3Real-World Content Recommendation Problem Loss Function
PayPal AI · PayPal Inc. · USA
WHALE: A Scalable Unified Model for Recommendation with Wukong-HSTU Architecture
Jul 19, 2026Renqin Cai, Dawei Sun, Yuanjun Yao +8Real-World Content Recommendation Problem Recommendation
Meta Platforms, Inc. USA
MESH: Scaling Up Retrieval with Heterogeneous Content Unification
Jul 14, 2026Jiaxing Qu, Yilin Chen, Junpeng Hou +4Real-World Content Recommendation Problem Sequential Recommendation
Pinterest Inc. San Francisco, CA, USA
Tokenizing Numerical and Embedding Features for LLM RecSys
Jul 10, 2026Zhe Xu, Ankit Peshin, Chiyu Zhang +7Real-World Content Recommendation Problem Soft-Token Representations
Meta
PIT-SUN: A Deployable Empirical Marginal Transform Framework with Expectation-Consistent Recovery for Regression in Recommender Systems
Jul 9, 2026Mingyu Zhao, Zhaohan Li, Zhenxiong Miao +4Real-World Content Recommendation Problem
1Renmin University of China · 2Kuaishou Technology
RankGraph-2: Lifecycle Co-Design for Billion-Node Graph Learning in Recommendation
Jun 16, 2026Renzhi Wu, Zikun Cui, Junjie Yang +10Graph-Based Retrieval Real-World Content Recommendation Problem
Meta MRS · Meta Training Data Infra · FB Monetization
DUET -- Dual User Embedding Transformers for Offsite Conversion Prediction
Jun 8, 2026Reazul Hasan Russel, Mingwei Tang, Rostam Shirani +18Click-Through Rate Prediction Real-World Content Recommendation Problem
AI at Meta
Time-Aware Diffusion based on Preference Disentanglement for Generative Recommendation
Jun 1, 2026Bangguo Zhu, Peng Huo, Yuanbo Zhao +3Real-World Content Recommendation Problem Preference Alignment Learning
Central South University Changsha, China · National Super Computing Center Tianjin, China · Renmin University of China Beijing, China +1
LeAP: Learnable Adaptive Permutation for Feature Selection in Heterogeneous and Sparse Recommender Systems
May 31, 2026Yihong Huang, Chen Chu, Fei Chen +3Feature Selection Real-World Content Recommendation Problem
Bilibili Inc., Shanghai, China
UniPinRec: Unifying Generative Retrieval and Ranking at Pinterest Scale
May 29, 2026Hanyu Li, Yi-Ping Hsu, Aditya Mantha +17Real-World Content Recommendation Problem Ranking
Pinterest
Context Features Are Cheap: Rank-Aware Decomposition for Efficient Feature Interaction in Recommender Systems
May 24, 2026Yevgeny TkachReal-World Content Recommendation Problem High-Order Correlations
Taboola · Israel
Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation
May 22, 2026Guoming Li, Shangyu Zhang, Junwei Pan +7Real-World Content Recommendation Problem Recommendation Quality
The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, China · Tencent Inc. Shenzhen, China
Building a privacy-preserving Federated Recommender system for mobile devices
May 21, 2026Aasheesh SinghReal-World Content Recommendation Problem Privacy-Preserving Framework
Université de Montréal · Département d’informatique et de recherche opérationnelle · Faculté des arts et des sciences
Spectral bandits for smooth graph functions with applications in recommender systems
May 19, 2026Tomáš Kocák, Michal Valko, Rémi Munos +2Real-World Content Recommendation Problem Bandits
SequeL team Inria Lille France · SequeL team, Inria France · Microsoft Research New England +2
Intelligent Elastic Feature Fading: Enabling Model Retrain-Free Feature Efficiency Rollouts at Scale
May 1, 2026Jieming Di, Xiaoyu Chen, Ying She +21Retraining Parallel Rollouts
Meta · USA
Think it, Run it: Autonomous ML pipeline generation via self-healing multi-agent AI
Apr 29, 2026Adela Bara, Gabriela Dobrita, Simona-Vasilica OpreaData Generation Pipelines Microservices
Bucharest University of Economic Studies, Department of Economic Informatics and Cybernetics, no.6 Piața Romană, Bucharest, 010374, Romania
CASP: Support-Aware Offline Policy Selection for Two-Stage Recommender Systems
Apr 24, 2026Nilson ChapagainReal-World Content Recommendation Problem Pessimism
Texas A&M University College Station, TX, USA
Pre-trained LLMs Meet Sequential Recommenders: Efficient User-Centric Knowledge Distillation
Apr 23, 2026Nikita Severin, Danil Kartushov, Vladislav Urzhumov +8Multimodal Recommendation Model Real-World Content Recommendation Problem
Independent researcher, Belgrade, Serbia · ITMO University, Saint Petersburg, Russia · Innopolis University, Innopolis, Russia +3
Spectral bandits for smooth graph functions
Apr 20, 2026Michal Valko, Rémi Munos, Branislav Kveton +1Real-World Content Recommendation Problem Bandits
INRIA Lille - Nord Europe, SequeL team, 40 avenue Halley 59650, Villeneuve d’Ascq, France · INRIA Lille - Nord Europe, SequeL team, France · Microsoft Research New England, Cambridge, MA, USA +1
HiSAC: Hierarchical Sparse Activation Compression for Ultra-long Sequence Modeling in Recommenders
Feb 24, 2026Kun Yuan, Junyu Bi, Daixuan Cheng +5Real-World Content Recommendation Problem Recommendation
Alibaba Group · Beijing, China · Renmin University
Macro Graph of Experts for Billion-Scale Multi-Task Recommendation
Jun 12, 2025Hongyu Yao, Zijin Hong, Hao Chen +6Real-World Content Recommendation Problem Multi--Task Learning
The Hong Kong Polytechnic University Hong Kong SAR, China · National University of Defense Technology Changsha, China · Beihang University Beijing, China
RecKG: Knowledge Graph for Recommender Systems
Jan 7, 2025Junhyuk Kwon, Seokho Ahn, Young-Duk SeoKnowledge Graphs Real-World Content Recommendation Problem
Inha University Incheon, South Korea
Personalized Execution Time Optimization for Billion-Scale Scheduled Jobs
Mar 11, 2022Yang Liu, Juan Wang, Idris Malik +7Schedulers Smooth Execution
Meta Hacker Way, Menlo Park, CA, USA, 94025

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Beyond Interaction Capacity: Estimator Scaling with Recursive Models for CTR Prediction

Shivang Chopra, Fotis Iliopoulos, Zsolt Kira, Gaurav Menghani

Georgia Institute of Technology · Google Research

Click-Through Rate Prediction Real-World Content Recommendation Problem Cross Validation Recursive Models Interaction Data

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Click-Through Rate prediction, a core task in recommendation and advertising systems, relies on modeling interactions among sparse categorical features. Explicit cross networks are a central paradigm for CTR prediction, and recent progress has largely come from increasing the interaction capacity of a single predictor through deeper cross networks and more expressive cross operators. We revisit whether continually increasing interaction capacity remains the most effective way to improve predictive performance, and find that its benefits quickly exhibit diminishing returns even as capacity continues to grow. This motivates a complementary scaling direction that we call estimator scaling, where additional resources are used to incorporate multiple related estimators rather than only enlarging a single predictor. Through theoretical analysis, we show that the gains from estimator scaling are governed by the amount of non-shared predictive variation available across estimators. However, exploiting this variation naively can be expensive: independently trained models provide substantial estimator diversity but require deployment cost to grow with ensemble size. This motivates a parameter-efficient realization of estimator scaling that can incorporate diversity from multiple estimator sources without maintaining multiple full models. Building on this view, we introduce RECursive Averaged Predictor (RECAP), a parameter-efficient recursive CTR model that operationalizes estimator scaling at three levels: distillation across independently trained models, exponential moving averaging over training trajectories, and aggregation over inference-time routes within a weight-shared recursive backbone. Experiments across multiple benchmarks establish new state-of-the-art predictive performance on standard benchmarks, while placing the RECAP on a favorable performance-parameter Pareto frontier.

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