Rethinking Recommendation Paradigms: From Pipelines to Agentic Recommender Systems

• URL: http://arxiv.org/abs/2603.26100v1
• Date: Fri, 27 Mar 2026 06:14:18 GMT
• Title: Rethinking Recommendation Paradigms: From Pipelines to Agentic Recommender Systems
• Abstract summary: Large-scale industrial recommenders typically use a fixed multi-stage pipeline (recall, ranking, re-ranking)<n>We propose an Agentic Recommender System (AgenticRS) that reorganizes key modules as agents.
• Score: 12.522583427934796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale industrial recommenders typically use a fixed multi-stage pipeline (recall, ranking, re-ranking) and have progressed from collaborative filtering to deep and large pre-trained models. However, both multi-stage and so-called One Model designs remain essentially static: models are black boxes, and system improvement relies on manual hypotheses and engineering, which is hard to scale under heterogeneous data and multi-objective business constraints. We propose an Agentic Recommender System (AgenticRS) that reorganizes key modules as agents. Modules are promoted to agents only when they form a functionally closed loop, can be independently evaluated, and possess an evolvable decision space. For model agents, we outline two self-evolution mechanisms: reinforcement learning style optimization in well-defined action spaces, and large language model based generation and selection of new architectures and training schemes in open-ended design spaces. We further distinguish individual evolution of single agents from compositional evolution over how multiple agents are selected and connected, and use a layered inner and outer reward design to couple local optimization with global objectives. This provides a concise blueprint for turning static pipelines into self-evolving agentic recommender systems.

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