Latest Recommendation Systems Research Papers

The newest Recommendation Systems papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Recommendation Systems so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• Generative Archetype-Grounded Item Representations for Sequential Recommendation
  Yifan Li, Jiahong Liu, Xinni Zhang, Hao Chen et al. · arXiv · Jun 9, 2026

  Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a critical bottleneck. While pre-trained large language models…

• From Prompt to Purchase: How AI Brand Recommendations Move Consumers on the Open Web
  Michael Iannelli, Alan Ai · arXiv · Jun 9, 2026

  When a conversational assistant recommends a brand to a user with no recent observed engagement, that user's same-name Google search rises +4.3 percentage points (pp) [3.1, 5.5], visits to the brand's own site +2.4 pp [1.4, 3.5], and brand-…

• SIDInspector: A Mapping-First Diagnostic Resource for Semantic-ID Tokenizers
  Jiandong Ding, Heng Chang, Huijie Qin, Tianying Liu · arXiv · Jun 9, 2026

  Semantic-ID (\sid) tokenizers are increasingly reused as standalone artifacts in generative recommendation: an exported item-to-code mapping becomes the address space that a later sequence generator must use. These mappings rarely come with…

• Atomic Intent Reasoning: Bringing LLM Semantics to Industrial Cross-Domain Recommendations
  Zhuohang Jiang, Yuxin Chen, Shijie Wang, Haohao Qu et al. · KDD · Jun 9, 2026

  Cross-domain recommendation is a core problem in content-to-e-commerce platforms. Its objective is to leverage user interactions with content to infer potential purchasing intent on the e-commerce side, thereby enhancing conversion rates an…

• $τ$-Rec: A Verifiable Benchmark for Agentic Recommender Systems
  Bharath Sivaram Narasimhan, Karthik R Narasimhan · arXiv · Jun 8, 2026

  As recommender systems transition toward agentic, multi-turn conversational interfaces, evaluation paradigms have struggled to keep pace. Current benchmarks often rely on "LLM-as-a-judge" evaluations, which introduce subjectivity, high cost…

• MetaPlate: Counterfactual-Guided RAG-LLM Tool for Personalized Food Recommendation and Hyperglycemia Prevention
  Asiful Arefeen, Carol Johnston, Hassan Ghasemzadeh · arXiv · Jun 8, 2026

  Postprandial hyperglycemia is a key risk factor for metabolic disorders; however, existing dietary guidance is often static, impractical, and insufficiently personalized, providing recommendations that are difficult to follow or not impactf…

• Mult-DPO: Multinomial Direct Preference Optimization for Recommender Systems
  Yaochen Zhu, Harald Steck, James McInerney, Aditya Sinha et al. · arXiv · Jun 8, 2026

  Direct preference optimization (DPO) is a simple and effective alignment strategy for large language models (LLMs) based on pairwise preferences. In recommender systems, however, user feedback is rarely pairwise. For a given context, e.g., …

• Popcorn: A Configurable Benchmark for Visual Evidence in Multimodal Movie Recommendation
  Ali Tourani, Fatemeh Nazary, Yashar Deldjoo, Tommaso Di Noia · arXiv · Jun 8, 2026

  Movies are long-form audiovisual works, yet recommender benchmarks often rely on trailers, thumbnails, or metadata. These sources differ in semantics and scalability: full movies preserve consumption-level evidence, trailers concentrate pro…

• Teach Multimodal Recommendation Model to See via Personalized Visual Extraction and Adaptive Learning
  Yutong Li, Xinyi Zhang, Ziyi Ye, Daoguo Dong et al. · arXiv · Jun 8, 2026

  Multimodal sequential recommendation (MSR) incorporates textual and visual information to improve recommendation quality. However, recent studies and our empirical analysis show that visual features are often underutilized, thereby contribu…

• Gryphon: A Unified Architecture for Semantic-ID Generation and Item-Level Scoring in Industrial Recommendations
  Daria Tikhonovich, Oleg Sorokin, Vladislav Dodonov, Mariia Ulianova et al. · arXiv · Jun 7, 2026

  Generative retrieval (GR) has become a scalable approach to candidate generation: each item is assigned a short hierarchical token sequence called a Semantic ID (SID), and the next item's SID is decoded autoregressively. A practical limitat…

• Adaptive Loss Balancing for Noise-Robust GRPO in Generative Recommendation
  Kewei Xu, Junbo Qi, Yanyan Zou, Pengfei Zhang et al. · arXiv · Jun 7, 2026

  Reinforcement learning (RL) presents a promising avenue for enhancing generative recommendation beyond supervised imitation, leveraging reward signals to guide policy improvement. However, its efficacy is critically contingent on the trustw…

• ToolRec: Calibrated Preference Alignment for Query Recommendation in On-Device Assistants
  Zihan Luo, Lingkui Chen, Ruike Zhang, Hong Huang et al. · arXiv · Jun 7, 2026

  Large Language Models (LLMs) have significantly advanced generative query recommendation. However, existing alignment methods primarily focus on standard chatbot scenarios, falling short in on-device intelligent assistants where users predo…

• OneFeed: A Unified Generative Framework for Feed Content Enhancement and Query Generation
  Guo Xun · arXiv · Jun 6, 2026

  Modern feed recommendation and search systems are deeply connected in user behavior butare usually modeled by separate architectures. Feed recommendation mainly captures implicitinterests from browsing interactions, while search systems rel…

• Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies
  Ekaterina Grishina, Stepan Kuznetsov, Askar Tsyganov, Ilya Ivanov et al. · arXiv · Jun 5, 2026

  The ranking of recommendation algorithms is a challenging problem since model performance is sensitive to dataset characteristics such as sparsity, sequential structure, and scale. This drives a demand for a proper methodology for fair comp…

• PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streams
  Fuqiang Wang, Song Tan, Zheng Guo, Jiaohao Fu et al. · arXiv · Jun 5, 2026

  Scientific paper recommendation is typically evaluated as static ranking over a fixed candidate set, yet real scientific reading unfolds as a daily, longitudinal process in which interests shift and feedback accumulates. We introduce PaperF…

• Gated Bidirectional Linear Attention for Generative Retrieval
  Artem Matveev, Vladislav Tytskiy, Sergei Makeev, Sergei Liamaev · arXiv · Jun 5, 2026

  In recommender systems, generative retrieval typically uses an encoder-decoder setup: an encoder processes a user interaction history, and an autoregressive decoder then generates recommended items. In large-scale streaming services, active…

• Decision-Theoretic Stopping Rules for Document Screening
  Aaron H. A. Fletcher, Mark Stevenson · arXiv · Jun 5, 2026

  Deciding when to stop reviewing the results of a search is a common problem with multiple applications. Existing stopping rules developed within Technology-Assisted Review (TAR) aim to achieve a pre-specified recall target and do not take i…

• SSRLive: Live Streaming Recommendation with Dynamic Semantic ID
  Teng Shi, Zhaoheng Li, Yuanhang Qu, Yi Liu et al. · arXiv · Jun 5, 2026

  Live streaming has emerged as one of the fastest-growing forms of online media, enabling instant content broadcasting and real-time engagement between users and streamers. Despite the effectiveness of existing recommendation algorithms in t…

• DREAM: Dynamic Refinement of Early Assignment Mappings
  Liwei Guan, Huanjie Wang, Hongwei Zhang, Linxun Chen et al. · arXiv · Jun 5, 2026

  Generative recommendation advances item retrieval by reformulating it as autoregressive generation of Semantic IDs (SIDs), compact token sequences that encode item semantics. While SIDs offer a strong semantic prior, current SID-based metho…

• Mind the Gap: Bridging Behavioral Silos with LLMs in Multi-Vertical Recommendations
  Nimesh Sinha, Raghav Saboo, Martin Wang, Sudeep Das · arXiv · Jun 4, 2026

  In multi-vertical e-commerce platforms like DoorDash, relatively newer product verticals such as grocery and retail present a significant opportunity for personalization innovation. A key challenge lies in solving the "cold start" problem f…

• OneReason Technical Report
  OneRec Team, Biao Yang, Boyang Ding, Chenglong Chu et al. · arXiv · Jun 4, 2026

  Generative recommendation models in the OneRec family have been widely deployed in many real-world services, such as short-video, live-streaming, advertising, and e-commerce. However, these generative models can only benefit from the scalin…

• Bridging the Semantic-Collaborative Gap: An Asymmetric Graph Architecture for Cold-Start Item Recommendation
  Anh Truong, John Trenkle, Yuanbo Chen, Honghong Zhao et al. · arXiv · Jun 4, 2026

  Collaborative filtering and graph-based recommendation models are highly effective because they leverage observed user interactions, but this dependence creates a fundamental cold-start challenge when newly added content has no interaction …

• WebKnoGraph: GNN-Powered Internal Linking
  Emilija Gjorgjevska, Georgina Mirceva, Miroslav Mirchev · arXiv · Jun 4, 2026

  Internal link optimization is a recurring task in search engine optimization, yet many production workflows rely on manual judgment, fixed page templates, or generic tool recommendations. Practitioners need ways to evaluate candidate links …

• ANCHOR: Agentic Noise Creation Framework for Human Simulation and Denoising Recommendation
  Xiangming Li, Hua Chu, Chengyu Feng, Jianan Li et al. · arXiv · Jun 4, 2026

  Distilling accurate user preferences from noisy implicit feedback remains a fundamental bottleneck in recommendation systems, highlighting the need for recommendation denoising. However, real-world data lack explicit noise annotations, forc…

• PHKT:Personalized Dynamic Hypergraph-enhanced KAN-Transformer for Multi-behavior Sequential Recommendation
  Ruijie Du, Hao Chen, Xin Zhang, Dongjing Wang et al. · arXiv · Jun 4, 2026

  In multi-behavior recommendation, auxiliary behaviors such as clicks, add-to-cart, and purchases can provide richer supervisory information for predicting target behaviors. Although existing graph and hypergraph methods are capable of model…

• Scaling Laws for Behavioral Foundation Models over User Event Sequences
  Rickard Brüel Gabrielsson · arXiv · Jun 3, 2026

  Foundation models are increasingly trained on sequences of user actions in recommendation, payments, fraud, and commerce, but these models still lack the kind of compute calibration that scaling laws provide for language models. We study a …

• Dual-Stream MLP is All You Need for CTR Prediction
  Kesha Ou, Zhen Tian, Wayne Xin Zhao, Long Zhang et al. · arXiv · Jun 3, 2026

  Click-through rate (CTR) prediction holds a pivotal role in online advertising and recommendation systems, where even small improvements can significantly boost revenue. Existing research primarily focuses on designing dual-stream architect…

• Dynamic Spectral Denoising with Global-Context Attention for Multi-Behavior Recommendation
  Miaomiao Cai, Yunshan Ma, Fangqi Zhu, Junfeng Fang et al. · KDD · Jun 1, 2026

  Multi-behavior recommendation improves target-behavior prediction by exploiting heterogeneous auxiliary feedback (e.g., view, collect, and cart), yet its robustness is undermined by behavior-dependent noise and inconsistency. We argue that …

• Rank-Constrained Deep Matrix Completion for Group Recommendation
  Mubaraka Sani Ibrahim, Lehel Csató, Isah Charles Saidu · arXiv · Jun 1, 2026

  The growing popularity of group activities has increased the need for methods that provide recommendations to groups of users given their individual preferences. Many existing group recommender systems rely on aggregating individual user pr…

• Decoupled Residual Quantization for Robust Semantic IDs in Recommendation
  Xuesi Wang, Junjie Wang, Ziliang Wang, Weijie Bian et al. · arXiv · Jun 1, 2026

  Semantic IDs represent items as shared discrete token sequences and have become a practical tool for recommendation and retrieval. Yet it remains difficult to tell why a tokenizer fails: poor quality may come from codebook underutilization,…