Agentic Recommender Systems
Foundations, Methods, Applications, and Advanced Topics
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Beschreibung
Recommender systems are entering a new era. As large language models and agent-based AI reshape what's possible, recommendation is no longer just a prediction problem: it's becoming a collaborative process where intelligent agents reason, communicate, coordinate, and adapt to ever-changing user needs.
Agentic Recommender Systems is a field that is exciting but still fragmented. Spanning recommender systems, LLMs, multi-agent systems, software engineering, and human-computer interaction, this book offers researchers, students, and practitioners a much-needed shared vocabulary and conceptual foundation. It moves from first principles to the frontier: grounding readers in how agentic approaches differ from traditional recommendation pipelines, proposing a general framework for reasoning about multi-agent recommendation, and tackling the open challenge of evaluation: datasets, metrics, baselines, and the limits of current methods. Along the way, real-world applications, architectural patterns, and the fast-growing ecosystem of agent frameworks and orchestration tools bring theory into practical focus.
Rather than claiming to have the final word on a still-maturing field, this book is designed as a resource to help readers navigate preference elicitation, continual user modeling, multi-agent coordination, and the broader implications of increasingly autonomous recommendation systems. Whether you're a graduate student entering the field, a researcher pushing its boundaries, or a practitioner building the next generation of intelligent recommendation technology, this book provides the coherent, comprehensive starting point the field has been missing.
Recommender systems are entering a new era. As large language models and agent-based AI reshape what's possible, recommendation is no longer just a prediction problem: it's becoming a collaborative process where intelligent agents reason, communicate, coordinate, and adapt to ever-changing user needs.
Agentic Recommender Systems is a field that is exciting but still fragmented. Spanning recommender systems, LLMs, multi-agent systems, software engineering, and human-computer interaction, this book offers researchers, students, and practitioners a much-needed shared vocabulary and conceptual foundation. It moves from first principles to the frontier: grounding readers in how agentic approaches differ from traditional recommendation pipelines, proposing a general framework for reasoning about multi-agent recommendation, and tackling the open challenge of evaluation: datasets, metrics, baselines, and the limits of current methods. Along the way, real-world applications, architectural patterns, and the fast-growing ecosystem of agent frameworks and orchestration tools bring theory into practical focus.
Rather than claiming to have the final word on a still-maturing field, this book is designed as a resource to help readers navigate preference elicitation, continual user modeling, multi-agent coordination, and the broader implications of increasingly autonomous recommendation systems. Whether you're a graduate student entering the field, a researcher pushing its boundaries, or a practitioner building the next generation of intelligent recommendation technology, this book provides the coherent, comprehensive starting point the field has been missing.
First unified framework connecting recommender systems, LLMs, and multi-agent AI into one coherent field Bridges theory and practice with real architectures, applications, and evaluation methods Serves as a launchpad for research and practice in a fast-moving, still-undefined frontier field
Autor*in
Ivens da Silva Portugal
Themen in »Agentic Recommender Systems«
agentic recommender system agentic AI LLM agent multi-agent large language model software engineering LLM recommender system