This paper proposes SCOReD, a framework for optimizing chain-of-thought (CoT) distillation in the recommendation domain by parsing teacher traces into typed segments, scoring their importance, and dyn…

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

LoopFM proposes a novel framework to significantly improve knowledge distillation for recommendation systems by structuring the rich intermediate embeddings of large foundation models as input feature…

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…

RecGPT-V3 is a stateful, hybrid-modal recommender system that uses a Memory Hub for user memory and a Hybrid-modal Foundation Model for joint reasoning over text tags and Semantic IDs, achieving consi…

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

The paper proposes a theoretical framework, called constraint-coupled reasoning, to make AI models less susceptible to knowledge distillation by coupling high-level capabilities to internal stability…

The paper proposes SPHERE, a novel framework that uses large language models to create semantic user personas, enabling effective cross-domain recommendation knowledge transfer between completely disj…

This paper introduces MARS, a framework for repeat-order food delivery recommendation that combines pre-trained language models with collaborative retrieval and contextual filtering.

The paper introduces Lossless Anti-Distillation Sampling (LADS), a novel sampling scheme that makes harvested data correlated for malicious distillers while ensuring benign users receive statistically…

This paper proposes SIREN, an automated method for manipulating the rankings of web-augmented large language models by iteratively editing retrieved webpages and testing the effect on the model's reco…

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…

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 adapts Large Language Models as semantic representation backbones in a two-tower retrieval architecture for high-throughput, large-scale recommendation systems.

The authors propose a new solution for the content cold-start problem in industry-scale search and recommender systems, reducing bias, improving model prediction, and validating long-term impact.

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 Popularity-Aware Denoising (PAD), a framework to improve denoising recommendation methods by modulating denoising strength based on item popularity.

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