A new framework, PRTA, is proposed for full-ranking recommendation tasks using large language models, where an LLM acts as a central planner and traditional recommendation models perform scoring.

This paper proposes a Bi-level Neural Architecture Search (Bi-NAS) framework to optimize explanations in recommender systems, refining cross-attention mechanisms and feature interaction functions whil…

The paper advocates for integrating explicit contextual feedback (like reviews and comments) into LLM-based recommender systems to achieve more personalized, transparent, and semantically aligned reco…

This paper proposes G2Rec, a scalable framework for industrial-scale generative recommendation that unifies graph-based user co-engagement modeling and semantic tokenization.

This paper investigates the effectiveness of stage-dependent preference elicitation strategies in conversational recommendation systems and introduces COPE, a novel architecture for strategy modeling.

This paper proposes RecRec, a recursive recommendation model that maintains a compact latent state and updates it through a shared recursive module conditioned on interaction evidence, using an eviden…

The paper introduces SafeRx-Agent, a knowledge-grounded multi-agent framework that improves medication recommendation accuracy and safety by incorporating fine-grained ATC codes and rigorous safety ve…

Taiji is a novel LLM-as-Enhancer framework that optimizes recommender systems by addressing the challenges of generating high-quality reasoning data and balancing semantic and ID-based rewards.

The paper introduces AIBuildAI-2, a knowledge-enhanced agent that significantly improves the automatic building of AI models by integrating an external, evolving knowledge system, achieving state-of-t…

This paper proposes ShopX, a model-centric framework for intent-driven shopping experiences using a single foundation model for intent understanding, execution planning, and item-space operations.

The paper proposes StARS, a model-agnostic framework for generating user-specific appropriateness scores for robot actions using collaborative filtering and learnable scene representations.

This paper proposes LBR, a framework to mitigate length bias in large language model-based recommendation systems.

The paper proposes OneReason, a framework that enhances the reasoning capability of generative recommendation models by focusing on improving item perception and structuring user behavior into coheren…

This paper proposes IntuRec, a two-stage framework for LLM-based recommendation that anchors latent reasoning with recommendation intuition.

MoG proposes a novel Mixture of Experts framework for graph-based RAG, which uses hub graphs to guide the sparse activation of domain-specific expert graphs, significantly improving retrieval accuracy…

This paper proposes conversational AI review assistants for code review, systems that engage in conversation with developers instead of just generating comments.

This paper proposes ChronoID, a framework for time-aware semantic ID learning in generative recommendation.