Recommender Systems

Follow this topic Ask about this topic

Momentum

32 papers in the last four weeks, up 28% on the four weeks before. 0.2% of all new papers.

Jul 13Week of Sep 28

Latest papers 304

All topics

SAERec: Constructing Fine-grained Interpretable Intents Priors via Sparse Autoencoders for Recommendation
Jun 17, 2026Jiangnan Xia, Xuansheng Wu, Yu Yang +2Sequential Recommendation Recommender Systems
University of Georgia United States · Shanghai AI Laboratory China · The Education University of Hong Kong Hong Kong +2
RankGraph-2: Lifecycle Co-Design for Billion-Node Graph Learning in Recommendation
Jun 16, 2026Renzhi Wu, Zikun Cui, Junjie Yang +10Graph Representation Learning Graph-Based Retrieval
Meta MRS · Meta Training Data Infra · FB Monetization
How Much Do Reviews Really Contribute? A Study on Text-Enriched Matrix Factorization for Recommendations
Jun 15, 2026Eduardo Ferreira da Silva, Mayki dos Santos Oliveira, Joel Machado Pires Denis Dantas Boaventura +1Feature Interaction Modeling Recommender Systems
Instituto de Computac¸˜ao – Universidade Federal da Bahia (UFBA) Salvador – BA – Brazil
Whose hotel does the AI recommend? An algorithm audit of reputation signals in LLM-assisted hotel selection
Jun 15, 2026Mirza Samad Ahmed Baig, Syeda Anshrah Gillani, Asher AliLLM Auditing Recommender Systems
Fandaqah, Al Khobar, Saudi Arabia. · Hamdard University, Karachi, Pakistan.
An Integrated System for Real-Time Student Assessment and Career Guidance Using Neural Networks in Computing Disciplines
Jun 14, 2026Sakir Hossain Faruque, Md. Jubair Hossain, Sharun Akter KhushbuEducational Assessment AI in Education
Department of Computer Science and Engineering, Daffodil International University, Dhaka-1216, Bangladesh · Department of Electrical and Electronic Engineering, Barishal Engineering College, Barishal, Bangladesh
Combining Retrieval-Augmented Text Generation with LLMs for Reading Content Recommendations
Jun 12, 2026Sooyeon Kim, Piotr S. MaciągRetrieval-Augmented Generation Controllable Text Generation
Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-661 Warsaw, Poland
Mood-Aware Music Recommendation: Integrating User Affective Signals into Ranking Systems
Jun 11, 2026Terence Zeng, Abhishek K. UmrawalLearning to Rank Recommender Systems
University of Illinois Urbana-Champaign, Urbana, Illinois 61801, USA
DiffCold: A Diffusion-based Generative Model for Cold-Start Item Recommendation
Jun 10, 2026Kangning Zhang, Yingjie Qin, Weinan Zhang +2Cold-Start Recommendation Recommender Systems
Shanghai Jiao Tong University, Shanghai, China · Xiaohongshu Inc., Shanghai, China
Automated Recommendation of Programming Learning Content Using Pattern-based Knowledge Components
Jun 9, 2026Muntasir Hoq, Griffin Pitts, Zhangqi Duan +5Computer Science Education AI in Education
North Carolina State University, Raleigh, NC, USA · University of Massachusetts, Amherst, MA, USA · University of Pittsburgh, Pittsburgh, PA, USA
Atomic Intent Reasoning: Bringing LLM Semantics to Industrial Cross-Domain Recommendations
Jun 9, 2026Zhuohang Jiang, Yuxin Chen, Shijie Wang +6LLM Inference Acceleration User Modeling
The Hong Kong Polytechnic University Hong Kong SAR, China · Kuaishou Technology Beijing, China
ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation
Jun 8, 2026Yihua Zhang, Mingfu Liang, Jiyan Yang +11Efficient Multimodal Inference Multimodal CoT Reasoning
Meta AI · Michigan State University · The University of North Carolina at Chapel Hill +1
$τ$ -Rec: A Verifiable Benchmark for Agentic Recommender Systems
Jun 8, 2026Bharath Sivaram Narasimhan, Karthik R NarasimhanAI Agent Reliability AI Agent Benchmarks
Independent Researcher Mountain View, CA, USA · Princeton University Princeton, NJ, USA
DBpedia-Enriched Company Representation for B2B Lead Recommendation
Jun 8, 2026Yuyan Qian, Claude Montacie, Milan Stankovic +1Representation Learning Recommender Systems
Sorbonne University, STIH, Paris, France · Leadbay, San Francisco, California
Adaptive Loss Balancing for Noise-Robust GRPO in Generative Recommendation
Jun 7, 2026Kewei Xu, Junbo Qi, Yanyan Zou +3Group Relative Policy Optimization Recommender Systems
Waseda University Tokyo, Japan · University of Electronic Science and Technology of China Chengdu, China
Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies
Jun 5, 2026Ekaterina Grishina, Stepan Kuznetsov, Askar Tsyganov +8Algorithm Selection Pairwise Comparison
HSE University Moscow, Russian Federation
PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streams
Jun 5, 2026Fuqiang Wang, Song Tan, Zheng Guo +8Information Retrieval Recommender Systems
1Key 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) · University of Chinese Academy of Science · 3Shanghai Artificial Intelligence Laboratory
Mind the Gap: Bridging Behavioral Silos with LLMs in Multi-Vertical Recommendations
Jun 4, 2026Nimesh Sinha, Raghav Saboo, Martin Wang +1Cold-Start Recommendation Recommender Systems
DoorDash Inc. San Francisco, California, USA
Bridging the Semantic-Collaborative Gap: An Asymmetric Graph Architecture for Cold-Start Item Recommendation
Jun 4, 2026Anh Truong, John Trenkle, Yuanbo Chen +4Cold-Start Recommendation Graph Representation Learning
Tubi San Francisco, USA · Kumo AI San Francisco, USA
QueryAgent-R1: Bridging Query Generation and Product Retrieval for E-Commerce Query Recommendation
Jun 4, 2026Dike Sun, Zheng Zou, Jingtong Zang +3E-Commerce Search Agentic Retrieval
Alibaba International Digital Commercial Group
Representation Curriculum: Stagewise Training for Robust Ranking and Allocation
Jun 3, 2026Ehsan Ebrahimzadeh, Sina Baharlouei, Abraham BagherjeiranCold-Start Recommendation Representation Learning
eBay Search Ranking and Monetization San Jose, California, USA
Distributional Approximate Nearest Neighbour Search for Uncertainty-Aware Retrieval
Jun 3, 2026Olivier JeunenRecommender Systems Approximate Nearest Neighbor Search
aampe · Antwerp, Belgium
Trading Engagement for Sustainability: Carbon-Aware Re-ranking for E-commerce Recommendations
Jun 3, 2026Noah Lund Syrdal, Anders Vestrum, Jorgen BerghLearning to Rank Recommender Systems
University of California, Berkeley
BAHSD: Bridging the Long-tail Gap via Adaptive Distillation in Black-box Sequential Recommendation
Jun 2, 2026Xi Zhou, Famin Wu, Mingming Li +4Sequential Recommendation Long-Tail Learning
Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China · School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China · Beijing Institute for General Artificial Intelligence, Beijing, China
A Locally Deployed RAG-Based Academic Advising System for Course Selection
Jun 2, 2026Feng Li, Yoritaka IwataEducational Technology Generative AI in Education
Shiga University, 1-1-1 banba, Hikone, Shiga 522-8522, Japan · Osaka University of Economics and Law, 2-10-45 Kitahonmachi, Yao, Osaka 581-8522, Japan
LLM-Assisted Reranking to Operationalize Nuanced Objectives in Recommender Systems
Jun 1, 2026Amir Ghasemian, Homa Hosseinmardi, Upasana Dutta +1LLM Prompting LLM Reranking
Department of Communication, University of California, Los Angeles, CA 90095 · Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104 · Annenberg School of Communication, University of Pennsylvania, Philadelphia, PA 19104 +1
Rank-Constrained Deep Matrix Completion for Group Recommendation
Jun 1, 2026Mubaraka Sani Ibrahim, Lehel Csató, Isah Charles SaiduRecommender Systems Matrix Completion
Department of Computer Science, African University of Science and Technology, Abuja, Nigeria. · Faculty of Mathematics and Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania. · Department of Computer Science, Baze University, Abuja, Nigeria.
Breaking the Information Silo: Semantic Personas for Cross-Domain Recommendation
Jun 1, 2026Jonathan Mayo, Moshe Unger, Konstantin BaumanCross-Domain Transfer Learning Recommender Systems
Technology and Information Management Department, Coller School of Management, Tel Aviv University, Tel Aviv 6997801, Israel · Management Information Systems Department, Fox School of Business, Temple University, Philadelphia, Pennsylvania 19122
Time-Aware Diffusion based on Preference Disentanglement for Generative Recommendation
Jun 1, 2026Bangguo Zhu, Peng Huo, Yuanbo Zhao +3Sequential Recommendation Recommender Systems
Central South University Changsha, China · National Super Computing Center Tianjin, China · Renmin University of China Beijing, China +1
FlowTime: Towards Continuous Generative Watch Time Prediction via Flow-based Personalized Priors
May 31, 2026Hongxu Ma, Han Zhou, Chenghou Jin +5Recommender Systems Flow-Based Generative Modeling
Fudan University Shanghai, China · Kuaishou Technology Beijing, China · Shanghai University of Finance and Economics Shanghai, China +1
LeAP: Learnable Adaptive Permutation for Feature Selection in Heterogeneous and Sparse Recommender Systems
May 31, 2026Yihong Huang, Chen Chu, Fei Chen +3Recommender Systems Feature Selection
Bilibili Inc., Shanghai, China

Jun 17, 2026, cs.IRJ/K move · Enter open · S save

SAERec: Constructing Fine-grained Interpretable Intents Priors via Sparse Autoencoders for Recommendation

Jiangnan Xia, Xuansheng Wu, Yu Yang, Xin Wang+1

University of Georgia United States · Shanghai AI Laboratory China · The Education University of Hong Kong Hong Kong+2

Sequential Recommendation Recommender Systems User Preference Modeling

Open paper AskPDF arXiv

Intent-based recommender systems have gained significant attention for improving accuracy and interpretability by modeling the underlying motivations behind user behaviors. Most existing models derive intents directly from user sequences via clustering or prototype learning. However, they are sensitive to sequence quality, require presetting the number of intents, and lack explicit semantic grounding. These issues lead to an incomplete and coarse intent set and limit the effectiveness of recommendation. In this paper, we propose the Sparse Autoencoder for intent-based recommendation (SAERec), a novel recommender that automatically constructs a fine-grained and interpretable intent space from a textual corpus to guide recommendation. Rather than treating texts as side signals, SAERec leverages them as high information density evidence for intent construction. Specifically, we first extract a comprehensive set of fine-grained interpretable intents from the latent space of large language models (LLMs) by using a sparse autoencoder (SAE) to disentangle and interpret text embeddings, which isolates intent-related semantics from textual noise. Then, for each user, we retrieve relevant intents from this set as priors to guide recommendation. It contains personal intents matching a user's current interests and public intents capturing general item patterns shared across users (e.g., quality, price). Finally, to integrate retrieved intents into sequence modeling, we propose a multi-branch attention mechanism that captures temporal dependencies and injects both personal and public intent signals, followed by an adaptive fusion layer to construct the final user representation for recommendation. Extensive experiments on public datasets demonstrate the superiority of SAERec, consistently outperforming state-of-the-art baselines while providing human-understandable explanations.

Momentum

Related topics

Latest papers 304