user modeling
30 articles about user modeling in AI news
SLSREC: A New Self-Supervised Model for Disentangling Long- and Short-Term User Interests in Recommendations
A new arXiv preprint introduces SLSREC, a self-supervised model that disentangles long-term user preferences from short-term intentions using contrastive learning and adaptive fusion. It outperforms state-of-the-art models on three benchmark datasets, addressing a core challenge in dynamic user modeling.
Tencent Launches 2025 Ad Algorithm Challenge with Massive All-Modality Recommendation Datasets
Tencent has launched an open competition and released two industrial-scale datasets (TencentGR-1M and TencentGR-10M) to advance generative recommender systems. This has spurred related research into debiasing techniques and novel reranking frameworks, moving the field toward more holistic, multi-modal user modeling.
IAT: Instance-As-Token Compression for Historical User Sequence Modeling
Researchers propose Instance-As-Token (IAT), which compresses all features of each historical interaction into a unified embedding token, then applies standard sequence modeling. This approach outperforms state-of-the-art methods and has been deployed in e-commerce advertising, shopping mall marketing, and live-streaming e-commerce with substantial business metric improvements.
New Research Proposes Stage-Wise Framework for Modeling Evolving User Interests in Recommendation Systems
arXiv paper introduces a unified neural framework that models both long-term preferences and short-term, stage-wise interest evolution for time-sensitive recommendations. Outperforms baselines on real-world datasets by capturing temporal dynamics more effectively.
VISTA: A Novel Two-Stage Framework for Scaling Sequential Recommenders to Lifelong User Histories
Researchers propose VISTA, a two-stage modeling framework that decomposes target attention to scale sequential recommendation to a million-item user history while keeping inference costs fixed. It has been deployed on a platform serving billions.
New MoE Framework Tames User Interest Shifts in Long-Sequence Recommendations
Researchers propose MoS, a model-agnostic MoE approach that handles long user sequences by detecting session hopping – where user interests shift across sessions. The theme-aware routing mechanism filters irrelevant sessions, while multi-scale fusion captures global and local patterns. Results show SOTA on benchmarks with fewer FLOPs than alternatives.
New AI Model Decomposes User Behavior into Multiple Spatiotemporal States
Researchers propose ADS-POI, which represents users with multiple parallel latent sub-states evolving at different spatiotemporal scales. This outperforms state-of-the-art on Foursquare and Gowalla benchmarks, offering more robust next-POI recommendations.
IPCCF: A New Graph-Based Approach to Disentangle User Intent for Better
A new research paper introduces Intent Propagation Contrastive Collaborative Filtering (IPCCF), a method designed to improve recommendation systems by more accurately disentangling the underlying intents behind user-item interactions. It addresses limitations in existing methods by incorporating broader graph structure and using contrastive learning for direct supervision, showing superior performance in experiments.
Multi-User LLM Agents Struggle: Gemini 3 Pro Scores 85.6% on Muses-Bench
A new benchmark reveals LLMs struggle with multi-user scenarios where agents face conflicting instructions. Gemini 3 Pro leads but only achieves 85.6% average, with privacy-utility tradeoffs proving particularly difficult.
LLM-Based System Achieves 68% Recall at 90% Precision for Online User Deanonymization
Researchers demonstrate that large language models can effectively deanonymize online users by analyzing their writing style and content across platforms. Their system matches 68% of true user pairs with 90% precision, significantly outperforming traditional methods.
FCUCR: A Federated Continual Framework for Learning Evolving User Preferences
Researchers propose FCUCR, a federated learning framework for recommendation systems that combats 'temporal forgetting' and enhances personalization without centralizing user data. This addresses a core challenge in building private, adaptive AI for customer-centric services.
ReFORM: A New LLM Framework for Multi-Factor Recommendation from User Reviews
Researchers propose ReFORM, a novel recommendation framework that uses LLMs to generate factor-specific user and item profiles from reviews, then applies multi-factor attention to personalize suggestions. It outperforms state-of-the-art baselines on restaurant datasets, offering a more nuanced approach to personalization.
Designing Cross-Sell Recommenders for High-Propensity Users: A Technical Approach
A technical article explores methods for debiasing popularity and improving category diversity in cross-sell recommendations, specifically targeting users with high purchase propensity. This addresses a core challenge in retail AI systems.
Spotify's Taste Profile Beta: A New Era of Transparent, User-Controlled Recommendation Systems
Spotify announced a beta feature called 'Taste Profile' that gives users direct control over their recommendation algorithms. This represents a significant shift toward transparent, interactive personalization in content platforms.
SRSUPM: A New Framework for Modeling Psychological Motivation Shifts in Sequential Recommendation
Researchers propose SRSUPM, a sequential recommender system framework that explicitly models users' evolving psychological motivations. It outperforms existing methods on three benchmarks by better capturing motivation shifts and collaborative patterns.
LLM-Driven Motivation-Aware Multimodal Recommendation (LMMRec): A New Framework for Understanding User Intent
Researchers propose LMMRec, a model-agnostic framework using LLMs to extract fine-grained user and item motivations from text. It aligns textual and interaction-based motivations, achieving up to 4.98% performance gains on three datasets.
Tuning-Free LLM Framework IKGR Builds Strong Recommender by Extracting Explicit User Intent
Researchers propose IKGR, a novel LLM-based recommender that constructs an intent-centric knowledge graph without model fine-tuning. It explicitly links users and items to extracted intents, showing strong performance on cold-start and long-tail items.
Multi-TAP: A New Framework for Cross-Domain Recommendation Using Semantic Persona Modeling
Researchers propose Multi-TAP, a cross-domain recommendation framework that models intra-domain user preference heterogeneity through semantic personas. It selectively transfers knowledge between domains, outperforming existing methods on real-world datasets.
The Agent-User Problem: Why Your AI-Powered Personalization Models Are About to Break
New research reveals AI agents acting on behalf of users create fundamentally uninterpretable behavioral data, breaking core assumptions of retail personalization and recommendation systems. Luxury brands must prepare for this paradigm shift.
Shopify Engineering details 'Flow generation through natural language'
Shopify Engineering describes a 2026 approach to generating complex workflows (flows) from natural language prompts using an agentic modeling framework, enabling non-technical users to create automation.
New Research Proposes Collaborative Contrastive Network for Generalizable
Researchers propose the Collaborative Contrastive Network (CCN) to solve Trigger-Induced Recommendation challenges in ephemeral e-commerce scenarios like Black Friday. Instead of modeling ambiguous intent, CCN learns context-specific preferences from user-trigger pairs via novel contrastive signals. In online A/B tests on Taobao, CCN increased CTR by 12.3% and order volume by 12.7% in unseen scenarios.
NextQuill: A Causal Framework for More Effective LLM Personalization
Researchers propose NextQuill, a novel LLM personalization framework using causal preference modeling. It distinguishes true user preference signals from noise in data, aiming for deeper personalization alignment beyond superficial pattern matching.
MCLMR: A Model-Agnostic Causal Framework for Multi-Behavior Recommendation
Researchers propose MCLMR, a causal learning framework that addresses confounding effects in multi-behavior recommendation systems. It uses adaptive aggregation and bias-aware contrastive learning to improve preference modeling from diverse user interactions like views, clicks, and purchases.
SELLER: A New Sequence-Aware LLM Framework for Explainable Recommendations
Researchers propose SELLER, a framework that uses Large Language Models to generate explanations for recommendations by modeling user behavior sequences. It outperforms prior methods by integrating explanation quality with real-world utility metrics.
LLM Agents Will Reshape Personalization
Researchers propose that LLM-based assistants are reconfiguring how user representations are produced and exposed, requiring a shift toward inspectable, portable, and revisable user models across services. They identify five research fronts for the future of recommender systems.
Personalized LLM Benchmarks: Individual Rankings Diverge from Aggregate (ρ=0.04)
A new study of 115 Chatbot Arena users finds personalized LLM rankings diverge dramatically from aggregate benchmarks, with an average Bradley-Terry correlation of only ρ=0.04. This challenges the validity of one-size-fits-all model evaluations.
New Research Models 'Exploration Saturation' in Recommender Systems
A research paper analyzes 'exploration saturation'—the point where more diverse recommendations hurt user utility. Findings show this saturation point is user-dependent, challenging the standard practice of applying uniform fairness or novelty pressure across all users.
Rapid Interest Shifts in Recommender Systems: A Case Study on Instagram Reels
A personal experiment demonstrates the remarkable speed at which Instagram's Reels recommendation system detects and responds to changes in user engagement patterns, highlighting the real-time adaptability of modern algorithms.
New Research Proposes Authority-aware Generative Retrieval (AuthGR) for
A new arXiv paper introduces an Authority-aware Generative Retriever (AuthGR) framework. It uses multimodal signals to score document trustworthiness and trains a model to prioritize authoritative sources. Large-scale online A/B tests on a commercial search platform report significant improvements in user engagement and reliability.
ContextSim: A New LLM Framework for Context-Aware Recommender System Simulation
A new arXiv preprint introduces ContextSim, a framework that uses LLM agents to simulate users interacting with recommender systems within realistic daily scenarios (time, location, needs). Experiments show it generates more human-aligned interactions and that RS parameters optimized with it yield improved real-world engagement.