Recommender Systems and Techniques

Neueste Paper

• Neither Black nor White: Balancing Semantic and Collaborative Signals with Graph-Informed Semantic IDs (GrIS)

  Aleksei Medvedev, Alejandro Ariza-Casabona, Steven Derby, Gonzalo Fiz Pontiveros, Xinyang Shao, Florian Spiess · 2. Oktober 2026

  Existing work on Semantic IDs (SIDs) for generative recommendation treats SID construction as a representation learning problem: encode items into a quantised latent space and read off codes. We argue this view is incidental. SID construction is, at heart, a recursive clustering problem, and once st…

• When More Data Is Not Enough: The Context-Sufficiency Frontier in Generative AI Personalization

  Merieme Askour, Ayoub Merimi · 2. Oktober 2026

  Personalization has long relied on customer data to infer what an individual is likely to value. We call this customer evidence: the customer's historical behavior and preferences. Generative AI extends personalization by allowing providers to supply changing situational information at the moment a …

• RealWorldShop: Benchmarking and Improving Conversational Shopping Agents in Real-World E-commerce

  Xinwei Yang, Kelong Mao, Yudong Guo, Sulong Xu, Simiu Gu, Chen Huang, Wenqiang Lei · 1. Oktober 2026

  Large language models are reshaping ecommerce from static recommenders into interactive shopping assistants, yet real-world shopping requires session-level decision support: users reveal and revise constraints, coordinate multiple goals, and expect product-grounded recommendations over a full conver…

• Rethinking Personalized Generation: Test-Time Alignment via Factorized Ranking Models

  Qiyao Ma, Junshan Zhang, Zhe Zhao · 30. September 2026

  Aligning large language models (LLMs) to diverse user preferences is fundamentally hindered by standard alignment paradigms that optimize for monolithic users. In this work, empirical studies are first used to reveal the existence of a massive, untapped performance headroom for personalized generati…

• RecKG: Knowledge Graph for Recommender Systems

  Junhyuk Kwon, Seokho Ahn, Young-Duk Seo · 30. September 2026

  Knowledge graphs have proven successful in integrating heterogeneous data across various domains. However, there remains a noticeable dearth of research on their seamless integration among heterogeneous recommender systems, despite knowledge graph-based recommender systems garnering extensive resear…

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

  Shivang Chopra, Fotis Iliopoulos, Zsolt Kira, Gaurav Menghani · 30. September 2026

  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…

• Challenges and Solutions for Bandits in the Wild: Warm-Started Mixture Bandits for Cross-Cohort Slate Recommendation

  Serafima Lebedeva, Sumantrak Mukherjee, Ali Arshad Sadal, Ilias Ek\c{s}i, Rahul Sharma, Julia Mueller, Theresa Dombrowski, Jakob Karolus, Viktor Bengs, Eyke H\"ullermeier, Sebastian Vollmer · 30. September 2026

  Many recommender services repeatedly encounter cold-start cohorts, where new users arrive with little or no interaction history. This creates two challenges: learning user preferences quickly from limited feedback and sustaining useful recommendations when each user has a finite catalog that can bec…

• ReMem: Rethinking Perception and Memory in Long-Context Recommendation Agents

  Haohao Qu, Yongcheng Jing, Chun Hin Chan, Shanru Lin, Wenqi Fan, Dacheng Tao · 30. September 2026

  Recent Recommendation Agents (RecAgents) offer a promising alternative by shifting recommendation to an active, user-side paradigm, where generative agents autonomously perceive external platforms, reason over user preferences, and execute decisions. However, existing RecAgents still suffer from two…

• FairDiff: Mitigating the Self-Reinforcing Matthew Effect in Diffusion Recommender Models

  Song-Li Wu, Xianquan Wang, Zhaocheng Du, Weinan Gan, Jingyi Wang · 30. September 2026

  While the "Matthew Effect" and filter bubbles are widely recognized outcome-level biases in recommender systems, we reveal that Diffusion Recommender Models (DRMs) uniquely compound this issue through their generative dynamics. Rather than merely inheriting data imbalances, DRMs trigger a self-reinf…

• FineSID: Scalable and Efficient Semantic Identifier Learning for Generative Recommendation

  Song-Li Wu, Weinan Gan, Zhaocheng Du, Xianquan Wang, Jingyi Wang · 30. September 2026

  A critical prerequisite of generative recommendation is designing semantic identifiers (SIDs) that are both scalable to large item sets and efficiently learnable. Existing SID learning methods fundamentally rely on Top-1 hard assignment during vector quantization. While heuristic strategies -- such …

• DP-Rec: Towards Dynamic Patching for Efficient Long-Sequence Recommendation

  Dwipam Katariya, Thomas Caputo, Akshat Shreemali, Juan Manuel Origgi, Nikita Seleznev, Pranab Mohanty, Kalanand Mishra, Nam Nguyen, James Montgomery · 29. September 2026

  Transformers have redefined sequential recommendation by effectively modeling dynamic user behaviors and long-range dependencies. However, they remain inherently inefficient: standard architectures operate at a fixed rate, allocating comparable computation to every item in a user's history regardles…

• Textual User Taste: Natural-Language User Context for Foundation-Model Recommender System at Scale

  Ghazal Fazelnia, Paul Gigioli, Eliza Klyce, Sharon Zheng, Katie Zelvin, Ye Myat Thein, Anurag Deshpande, Seda Davtyan, Kate Remeika, Maya Hristakeva, Erik Franco, Karen Banzon, Peng Ge, Jacqueline Wood, Nandini Singh, David Murgatroyd, Mounia Lalmas, Yves Raimond, Andreas Damianou · 29. September 2026

  Foundation model recommender systems require user context that can be consumed by large language models, reasoned over, and refined through natural-language interaction. Traditional behavioral embedding vectors remain highly effective for retrieval and ranking, but they are opaque to users and not n…

• KuaFu: Compressing Long User Behavior into Understanding at Billion Scale

  Jiahao Hui, Lin Zhu, Yishen Hu, Jingdong Shu, Zetai Jiang, Xining Ran, Ben Tan, Yeshou Cai, Gong Chen, Haijie Gu, Jie Jiang · 28. September 2026

  Conversational agents, generative recommenders, and personalized advertising all rest on one capability: understanding each user from raw behavior. Prevailing industrial practice is task-specific: for each task, a relevant subsequence is extracted from the full history and a dedicated model trained …

• AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Side

  Ryoma Sato · 28. September 2026

  Recommender systems have traditionally been developed for platforms. However, this has given rise to many phenomena that may be advantageous for platform lock-in but are a nuisance to users, such as clickbait, filter bubbles, and the spread of fake news. Recently, user-side recommender systems have …

• SPADE: Escaping the Popularity-Similarity Frontier to Measure Serendipitous Recommendations

  Tobias Vente, Maarten Peirsman, Noah Dani\"els, Hannu Toivonen, Bart Goethals · 28. September 2026

  Recommender systems engineer serendipity to foster active exploration and break predictable consumption cycles. The problem with existing offline beyond-accuracy metrics is that they often either isolate historical similarity or global popularity. We aim to design an evaluation metric that examines …

• Bootstrapping Conversational Recommendation Agents At Spotify: Synthetic Data Generation and Self-Improvement Loops

  Enrico Palumbo, Alexandre Tamborrino, Victor Ode, Ben Lacker, Adri\`a Casas Escoda, Jeremy Hopple, Marcus Better, James Leoni, Hugo Galv\~ao, Hugues Bouchard, Mounia Lalmas, Jos\'e Luis Redondo Garc\'ia, Abenezer Abebe, Ann Clifton, Anton Blomberg, Henrik Lindstr\"om, Dani Doro, Christine Doig Cardet · 28. September 2026

  Conversational recommendation agents are a new paradigm for content discovery, enabling users to express complex intents through natural language (e.g., "recommend Italian indie artists I haven't heard before"). A central challenge in building such agents is optimizing agent planning -- deciding how…

• T-RoPE: Time-Aware Rotary Position Embedding for Sequential Recommendation

  Yang Liu, Noel Loo, Ali Khanafer, Shuying Sun, Akshay Soni, Zhong Wu, Linjun Yang · 28. September 2026

  Large-scale recommenders increasingly adopt the sequential generative recipe behind large language models, bringing the Transformer into recommendation along with design choices made for text, including Rotary Position Embedding (RoPE). In language models, RoPE encodes token indices for relative pos…

• From Interests to Semantic IDs: Retrieval-Grounded Credit Assignment for Generative Recommendation

  Mengdan Zhu, Yufan Zhao, Yao Zhao, Sophie Di, Tao Di, Yulan Yan, Sridhar Iyer, Liang Zhao · 25. September 2026

  Semantic IDs (SIDs) encode each catalog item as a short token sequence, enabling generative recommenders to predict the next item autoregressively. Reasoning-enhanced variants, an increasingly common extension, first generate a textual trace and then decode a next-item SID by beam search. Such recom…

• Learning Better Reasoning for Generative Recommendation with Semantic IDs

  Mengdan Zhu, Yufan Zhao, Sophie Di, Yao Zhao, Tao Di, Yulan Yan, Sridhar Iyer, Liang Zhao · 25. September 2026

  Generative recommendation reformulates item retrieval as sequence generation, allowing a unified model to directly generate the next item from a user's interaction history. Semantic IDs further make this paradigm effective and scalable by representing each item as discrete codes, enabling knowledge …

• Cross-Country Code-Mixing for Generative Recommendation

  Yuan Gao, Hao Deng, Haibo Xing, Yi Xu, Lingyu Mu, Jinxin Hu, Yu Zhang, Xiaoyi Zeng · 25. September 2026

  Cross-country recommendation on modern e-commerce platforms is typically deployed with disjoint user and item ID spaces across markets, removing the shared anchors that conventional cross-domain methods rely on. Generative recommendation (GR) mitigates this by mapping items into a shared token space…

• Learned Cross-Task Relationships in Multi-Task Models

  Victor Zhang, Yiping Yuan, Florian Raudies, Bosun Adeoti, Brian Y. C. Leung, Sanjay Surendranath Girija, Naijing Zhang · 25. September 2026

  We propose a framework that learns cross-task relationships in multi-task models by approximating the joint distribution of task labels through targeted pairwise relationships. This approach improves performance via transfer learning and enhances information extraction without the intractable comple…

• A Systematic Benchmark of Explainable Methods for Temporal Attribution in Sequential Recommendation Systems

  Akash Pandey, Kanisha Shah, Addrish Roy, Dwipam Katariya, Hongyangyang Shi, Amanda Ding, Kalanand Mishra, Pranab Mohanty · 24. September 2026

  Sequential RecSys are central to modern personalization, exploiting user's historical interaction sequences to drive next-step decisions. Deep learning models, particularly CNN and Transformer-based architectures, have proven highly effective at capturing temporal dependencies in these histories. Fo…

• TailSpec-EASE: Knowledge-Graph-Regularized Linear Recommendation for Web Long-Tail Discovery

  Jianru Shen · 23. September 2026

  Recommender systems on Web platforms tend to over-serve popular items and neglect the long tail. Item-side knowledge graphs (KGs), often available as linked data or RDF-style Web resources, can help by connecting sparse items through shared semantic attributes. Many competitive KG-aware recommenders…

• Lightweight Ranking Heads: Accelerating Multi-Task Experimentation in Production Recommender Systems

  Sanjay Surendranath Girija, Aniruddh Nath, Li Wei, Yanhao Jiang, Shawn Andrews, Lukasz Heldt, Yi Wu, Aditya Mahajan, Mohit Sharma · 23. September 2026

  Modern production-scale recommender systems rely on complex, multi-task ranking models. Introducing new prediction tasks into these massive systems often causes bottlenecks - it risks negative task conflicts with existing tasks, and can lead to long development and experimentation cycles due to the …

• A Behavioral Trait Leaks into Preferences: Diagnosing Trait Interference in LLM User Simulators

  Chaehyun Kim, Sein Kim, Hongseok Kang, Chanyoung Park · 23. September 2026

  LLM-based user simulators aim to bridge the offline-online gap in recommender evaluation by emulating users through injected traits, where preference attributes determine what a user engages with and a behavioral activity trait governs how long they browse. However, we show this intended trait indep…