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…

A novel structure-aware reinforcement learning-based method is proposed to exacerbate unfairness in recommender systems by modeling structural and sequential dependencies.

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…

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 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…

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

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

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 proposes HyCoRec, a method to alleviate the Matthew effect in conversational recommendation by learning multi-aspect preferences.

The paper proposes ProRL, an effective Reinforcement Learning framework that rectifies gradient estimation deficiencies to optimize proactive recommendation paths, significantly outperforming existing…

This paper presents PermR, a lightweight algorithm for reranking search results in e-commerce platforms to maximize revenue while preserving relevance and other constraints.

This paper introduces Project Kairos, a framework for algorithmic news personalization in resource-constrained environments using contextual online learning and Cholesky factor updates.

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 TDPM, a time-aware diffusion model for generative recommendation, which significantly improves recommendation accuracy by explicitly modeling the non-stationary, time-evolving natur…

This paper proposes Expert-Following Strategies, a framework that identifies top-performing investors based on historical ROI and recommends assets they purchased, scoring by ROI-weighted purchase fre…

This paper presents a human-in-the-loop framework for providing personalized recourse in machine learning, using iterative Bayesian inference for causal model approximation.

This paper proposes a mood-conditioned ranking framework for music recommendation systems using user affective signals in the energy-valence space.

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