Pith. sign in

REVIEW 10 cited by

A Survey on LLM-powered Agents for Recommender Systems

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2502.10050 v1 pith:SIEMERJW submitted 2025-02-14 cs.IR cs.AI

Qiyao Peng , Hongtao Liu , Hua Huang , Qing Yang , Minglai Shao This is my paper
classification cs.IRcs.AI
keywords agentsrecommendersystemsapproachesllm-poweredresearchanalyzecomplex
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Recommender systems are essential components of many online platforms, yet traditional approaches still struggle with understanding complex user preferences and providing explainable recommendations. The emergence of Large Language Model (LLM)-powered agents offers a promising approach by enabling natural language interactions and interpretable reasoning, potentially transforming research in recommender systems. This survey provides a systematic review of the emerging applications of LLM-powered agents in recommender systems. We identify and analyze three key paradigms in current research: (1) Recommender-oriented approaches, which leverage intelligent agents to enhance the fundamental recommendation mechanisms; (2) Interaction-oriented approaches, which facilitate dynamic user engagement through natural dialogue and interpretable suggestions; and (3) Simulation-oriented approaches, which employ multi-agent frameworks to model complex user-item interactions and system dynamics. Beyond paradigm categorization, we analyze the architectural foundations of LLM-powered recommendation agents, examining their essential components: profile construction, memory management, strategic planning, and action execution. Our investigation extends to a comprehensive analysis of benchmark datasets and evaluation frameworks in this domain. This systematic examination not only illuminates the current state of LLM-powered agent recommender systems but also charts critical challenges and promising research directions in this transformative field.

Discussion (0). Sign in to comment.

Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Attacking and Defending Multi-Agent Collaborative Filtering Systems Through Connectivity

    cs.IR 2026-08 conditional novelty 6.0 of 10

    In agent-based collaborative filtering, attack spread and privacy leakage grow with interaction connectivity, but the effect is asymmetric between user and item agents and differs between early and steady-state phases.

  2. HyperAgent4POI: Dynamic Semantic Message Passing on Multi-Agent Hypergraphs for Missing-Modality Recommendation

    cs.IR 2026-08 conditional novelty 6.0 of 10

    HyperAgent4POI couples per-layer LLM-based modality completion with soft hypergraph incidence refinement, reporting NDCG@20 gains of 7.4 to 9.4 percent over the strongest baseline at a 60 percent modality-missing rate.

  3. Are LLMs Reliable Rankers? Rank Manipulation via Two-Stage Token Optimization

    cs.CL 2025-10 conditional novelty 6.0 of 10

    RAF, a two-stage token-optimization attack, creates brief natural-sounding text injections that reliably boost a target item's rank in LLM reranking outputs, beating state-of-the-art baselines in effectiveness, stealt...

  4. The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems

    cs.IR 2025-07 conditional novelty 6.0 of 10

    A framework for agentic recommender systems plus a pilot study showing multi-agent pipelines beat a single-shot LLM only on high-diversity user histories.

  5. The AI Wave and the Reinvention of Game Discovery: Oversupply, Structural Correction, and Agentic Player-Game Matching

    cs.HC 2026-07 conditional novelty 5.0 of 10

    Game oversupply is a concentration correction, not a crash; persona-style cold-start matching and redistributive access payouts can lift median per-title developer revenue.

  6. SR-Agent: An Experience-Driven Agentic Framework for Post-Ranking Strategy Refinement in E-Commerce Recommendation

    cs.AI 2026-07 conditional novelty 5.0 of 10

    SR-Agent is an LLM-agent pipeline that automatically inspects, diagnoses, and updates post-ranking recommendation strategies; a one-month Kuaishou A/B test reports +0.71% order volume, +0.34% browsing depth, and +0.48...

  7. Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems

    cs.IR 2026-07 accept novelty 5.0 of 10

    Agentic recommender systems are organized by agent role (assisted, as-recommender, as-simulator) crossed with autonomy levels L2–L5, yielding a roadmap of architectures, evaluation limits, and open challenges.

  8. RecoWorld: Building Simulated Environments for Agentic Recommender Systems

    cs.IR 2025-09 conditional novelty 5.0 of 10

    A design proposal, not a tested system: a dual-view simulation loop in which an LLM-simulated user issues reflective instructions when about to disengage, and an instruction-following recommender adapts to maximize si...

  9. Rethinking Group Recommender Systems in the Era of Generative AI: From One-Shot Recommendations to Agentic Group Decision Support

    cs.IR 2025-07 conditional novelty 5.0 of 10

    The paper proposes reorienting group recommender systems from one-shot preference aggregation to chat-based, agentic decision support powered by large language models.

  10. LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking

    cs.IR 2025-06 conditional novelty 4.0 of 10

    A KG-enhanced LlamaRec that feeds user-specific relation paths into a Llama-2 ranker reports modest MRR, NDCG, and Recall gains on two benchmarks.

Pith tools