REVIEW 18 cited by
Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models
Not yet reviewed by Pith; the record is open.
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
Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models
read the original abstract
Recent breakthroughs in Large Language Models (LLMs) have led to the emergence of agentic AI systems that extend beyond the capabilities of standalone models. By empowering LLMs to perceive external environments, integrate multimodal information, and interact with various tools, these agentic systems exhibit greater autonomy and adaptability across complex tasks. This evolution brings new opportunities to recommender systems (RS): LLM-based Agentic RS (LLM-ARS) can offer more interactive, context-aware, and proactive recommendations, potentially reshaping the user experience and broadening the application scope of RS. Despite promising early results, fundamental challenges remain, including how to effectively incorporate external knowledge, balance autonomy with controllability, and evaluate performance in dynamic, multimodal settings. In this perspective paper, we first present a systematic analysis of LLM-ARS: (1) clarifying core concepts and architectures; (2) highlighting how agentic capabilities -- such as planning, memory, and multimodal reasoning -- can enhance recommendation quality; and (3) outlining key research questions in areas such as safety, efficiency, and lifelong personalization. We also discuss open problems and future directions, arguing that LLM-ARS will drive the next wave of RS innovation. Ultimately, we foresee a paradigm shift toward intelligent, autonomous, and collaborative recommendation experiences that more closely align with users' evolving needs and complex decision-making processes.
Forward citations
Cited by 18 Pith papers
-
Do Recommendation Algorithms Work When Users Are LLM Agents? A Case Study on Moltbook
Item-side structural recommenders outperform user-personalization methods on Moltbook because LLM agents produce stationary, structure-driven engagement rather than learnable preferences.
-
F-GRPO: Factorized Group-Relative Policy Optimization for Unified Candidate Generation and Ranking
F-GRPO factorizes group-relative policy optimization into generation and ranking phases within one autoregressive sequence, using order-invariant coverage and position-aware utility rewards to improve top-ranked perfo...
-
RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systems
RecRM-Bench is a new large-scale benchmark dataset and framework for multi-dimensional reward modeling in agentic recommender systems, spanning instruction following, factual consistency, query-item relevance, and use...
-
OLIVIA: Online Learning via Inference-time Action Adaptation for Decision Making in LLM ReAct Agents
OLIVIA treats LLM agent action selection as a contextual linear bandit over frozen hidden states and applies UCB exploration to adapt online, yielding consistent gains over static ReAct and prompt-based baselines on f...
-
RecoAtlas: From Semantic Plausibility to Set-Level Utility in LLM Recommendation Agents
RecoAtlas is a benchmark that evaluates LLM recommendation agents on behavior-grounded metrics for relevance, complementarity, and diversity in addition to semantic coherence.
-
Skill-CMIB: Multimodal Agent Skill for Consistent Action via Conditional Multimodal Information Bottleneck
CMIB uses a conditional multimodal information bottleneck to create reusable agent skills that separate verbalizable text content from predictive perceptual residuals, improving execution stability.
-
Purifying Multimodal Retrieval: Fragment-Level Evidence Selection for RAG
FES-RAG reframes multimodal RAG as fragment-level selection using Fragment Information Gain to outperform document-level methods with up to 27% relative CIDEr gains on M2RAG while shortening context.
-
TRACE: A Conversational Framework for Sustainable Tourism Recommendation with Agentic Counterfactual Explanations
TRACE is a multi-agent LLM-based conversational framework that generates sustainable tourism recommendations via counterfactual explanations and clarifying questions to balance user relevance with environmental impact.
-
Three-Body Alignment: Aligning Chess Agent with Human Reasoning through Reranked Rationale
Reranking retrieved grandmaster rationales by FEN similarity raises a chess LLM's semantic alignment with grandmaster explanations from 0.61 to 0.73 cosine similarity, while reducing tactical quality.
-
Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable
A behavior-centered handbook generated from agent-harness code helps LLM planners find the right edit sites and produce better edit plans than direct repository exploration.
-
Do Recommendation Algorithms Work When Users Are LLM Agents? A Case Study on Moltbook
On the Moltbook platform populated by LLM agents, popularity-based and item-side collaborative filtering methods outperform user-representation techniques for predicting next forum engagement.
-
Factorized Latent Reasoning for LLM-based Recommendation
FLR factorizes latent reasoning into multiple preference factors using multi-factor attention and regularizations, outperforming baselines on recommendation benchmarks while adding robustness and interpretability.
-
MEG-RAG: Quantifying Multi-modal Evidence Grounding for Evidence Selection in RAG
MEG-RAG defines a new MEG metric based on Semantic Certainty Anchoring and trains a multimodal reranker to select evidence aligned with ground-truth semantic anchors, yielding higher accuracy and consistency on the M²...
-
Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems
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.
-
Beyond Item Order: Temporal Gap Tokenization for Generative Recommendation with Semantic IDs
Interleaving fixed log-scale gap tokens with semantic IDs, plus TA-FAMAE temporal regularization, consistently beats ReSID and other SID generative baselines on Amazon sequential recommendation.
-
SmartWalkCoach: An AI Companion for End-to-End Walking Guidance, Motivation, and Reflection
A three-agent mobile system for end-to-end walking support shows motivational companion dialogue boosts affect and UX in a 12-person in-the-wild crossover study.
-
A Position Paper on Recommender Systems in the Era of Autonomous Agents
A position paper proposes that transaction-oriented recommender systems be redesigned around client-side autonomous agents that query, compare, and verify options across platforms.
-
Fair Agents: Balancing Multistakeholder Alignment in Multi-Agent Personalization Systems
The authors propose a conceptual framework integrating stakeholder-LLM alignment methods, social choice-based aggregation for collective decisions, and stakeholder-centric evaluations to achieve fair multi-agent perso...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.