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

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 proposes Mem-GF, a memory-efficient graph filtering-based collaborative filtering method that approximates polynomial graph filters using Krylov subspaces, achieving significant memory savi…

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…

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…

MARS proposes an encoder-agnostic aggregation operator that explicitly models multi-scale temporal structure in sequential recommendation, achieving state-of-the-art performance across both sparse and…

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 introduces UniRank, an open benchmark for comparing and studying unified ranking models that combine sequential modeling and feature interaction.

The paper proposes a context-weighted sampler and scaled cross-entropy loss function for discrete generative modeling to improve generation quality and reduce generative perplexity.

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

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

This paper proposes a two-stage clustering algorithm to ensure per-sample guardrails in LLM-based applications, reducing inference cost and latency.