A simple graph heuristic without training or sequence encoders matches or outperforms trained generative recommenders on 10 of 14 sequential recommendation benchmarks by exploiting local transition and feature shortcuts.
Learnable item tokenization for generative recommendation
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
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2026 4representative citing papers
Agentic Recommender Systems turn static recommendation pipelines into self-evolving collections of agents using reinforcement learning and LLM-driven architecture generation.
AutoModel uses three core agents (AutoTrain, AutoFeature, AutoPerf) connected by a shared coordination layer to automate model design, feature evolution, performance management, and paper-driven reproduction in large-scale recommender systems.
UxSID introduces semantic-group shared interest memory with Semantic IDs and dual-level attention to model ultra-long user sequences, claiming state-of-the-art results and a 0.337% revenue lift in advertising A/B tests.
citing papers explorer
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An Embarrassingly Simple Graph Heuristic Reveals Shortcut-Solvable Benchmarks for Sequential Recommendation
A simple graph heuristic without training or sequence encoders matches or outperforms trained generative recommenders on 10 of 14 sequential recommendation benchmarks by exploiting local transition and feature shortcuts.
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Rethinking Recommendation Paradigms: From Pipelines to Agentic Recommender Systems
Agentic Recommender Systems turn static recommendation pipelines into self-evolving collections of agents using reinforcement learning and LLM-driven architecture generation.
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AgenticRS-Architecture: System Design for Agentic Recommender Systems
AutoModel uses three core agents (AutoTrain, AutoFeature, AutoPerf) connected by a shared coordination layer to automate model design, feature evolution, performance management, and paper-driven reproduction in large-scale recommender systems.
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UxSID: Semantic-Aware User Interests Modeling for Ultra-Long Sequence
UxSID introduces semantic-group shared interest memory with Semantic IDs and dual-level attention to model ultra-long user sequences, claiming state-of-the-art results and a 0.337% revenue lift in advertising A/B tests.