Recommender Systems

Discover the AI behind what you watch, buy, and read. This category explores Recommender Systems, diving into the machine learning algorithms that drive highly personalized digital experiences 🛍️. From collaborative filtering to deep sequence modeling, explore the research that helps algorithms understand human behavior and connect users with exactly what they are looking for.

FCUCR: Federated Continual Recommendation That Remembers You Without Storing. Your Data.

FCUCR: Federated Continual Recommendation That Remembers You Without Storing Your Data

Leave a Comment / Machine Learning, Cybersecurity, Recommender Systems /

FCUCR: Federated Continual Recommendation That Remembers You Without Storing Your Data | AI Trend Blend AITrendBlend Machine Learning Computer Vision NLP Recommenders System About Recommender Systems · Federated AI · ACM Web Conference 2026 (WWW ’26) · arXiv:2603.17315 · 16 min read FCUCR: The Recommender System That Learns Who You’re Becoming — Without Ever Seeing […]

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PRECTR-V2: How Alibaba Solved Cold-Start, Exposure Bias, and a Frozen Encoder — All in One Unified Search Ranking Framework.

PRECTR-V2: How Alibaba Solved Cold-Start, Exposure Bias, and Frozen Encoders in One Unified Search Ranking Framework

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PRECTR-V2: How Alibaba Solved Cold-Start, Exposure Bias, and Frozen Encoders in One Unified Search Ranking Framework | AI Trend Blend AITrendBlend Machine Learning About Recommendation Systems · arXiv:2602.20676 · Alibaba Group / Xianyu · 18 min read PRECTR-V2: How Alibaba Solved Cold-Start, Exposure Bias, and a Frozen Encoder — All in One Unified Search Ranking

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MaRI: The Structural Re-parameterization Breakthrough That Eliminated Redundant Computation in Kuaishou’s Ranking Models.

MaRI: How Kuaishou Solved the Hidden Redundancy Problem Plaguing Recommendation Models

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MaRI: How Kuaishou Solved the Hidden Redundancy Problem Plaguing Recommendation Models | AI Systems Research AISecurity Research Machine Learning Cybersecurity About Recommendation Systems · arXiv:2602.23105v1 [cs.IR] · 14 min read MaRI: The Structural Re-parameterization Breakthrough That Eliminated Redundant Computation in Kuaishou’s Ranking Models How a team of researchers at Kuaishou discovered that the biggest bottleneck

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Dynamics of Learning under User Choice: Overspecialization and Peer-Model Probing.

How AI Platforms Get Trapped Serving Only Their Fans—and the peer-model PROBING Fix That Breaks the Cycle

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How AI Platforms Get Trapped Serving Only Their Fans—and the Peer-Probing Fix That Breaks the Cycle | AI Systems Research AISecurity Research Machine Learning About Multi-Agent Learning · arXiv:2602.23565v1 [cs.LG] · 16 min read The Overspecialization Trap: Why Competing AI Platforms Inevitably Become Echo Chambers—and How Peer Probing Breaks the Cycle Researchers from UW and

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