This paper studies a dynamic assortment problem on a two-sided service platform with incomplete information and heterogeneous customers, and develops a data-driven algorithm to learn parameters and op…

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…

The paper proposes GREW, a novel Green-REd Watermarking framework that embeds ownership signals into recommender systems' intrinsic ranking process without requiring synthetic data, achieving robust p…

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

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

The paper proposes Group Rank-Constrained Deep Matrix Completion (Group RC-DMC), a novel framework that jointly leverages low-rank structure and attention-based modeling to provide accurate group reco…

The paper proposes DINOSAUR, a framework that incorporates embedding uncertainty into Approximate Nearest Neighbour search to improve retrieval for niche, long-tail content.

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

The paper introduces a novel paradigm where a fine-tuned LLM acts as an ancillary predictor to forecast likely advertisers, significantly improving ad recommendation systems by augmenting candidate ge…

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…

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…

The study demonstrates that conditioning AI brand recommendations on a user's persona significantly alters the recommended product set, particularly for mid-market brands, and this effect is largest o…

The paper proposes Shallow-RHS, an asymmetric graph-completion model, to solve the cold-start problem for both new content and new devices in large-scale recommendation systems.

The paper introduces STORYLENSWRITER, a novel framework that significantly improves personalized story rewriting by incorporating context-aware narrative enrichment, outperforming style-only adaptatio…

The paper introduces SCALR, a novel framework that generates synthetic user-item interaction data from a source domain to augment a target recommendation domain, significantly improving system perform…

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

This paper compares traditional machine learning models (Random Forests, XGBoost, SVM) against a complex Unified Multi-Task Time Series Model for churn prediction, concluding that conventional methods…