This paper introduces a novel framework, CFALR, that combines collaborative filtering and large language models for personalized outfit recommendation.

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 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.

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

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

A new approach to jointly evaluate fairness and relevance in recommender systems is proposed.

This paper proposes a generative recommendation framework called SPAR that improves personalization and balances historical interaction loads in semantic ID-based recommendation systems.

This paper proposes a new framework for offline recommendation evaluation that is both reliable and explainable.

A visual analytics approach for item-based ranking that combines model-driven active learning with human-driven item-selection methods.

This paper introduces KuaiRecV2, a benchmark for recommender systems that addresses inconsistencies between offline and online evaluations.

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

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 introduces AutoRSE, an evaluation framework for Recommender Systems Engineer Agents (RSE-Agents) and conducts a large-scale study to understand their capabilities and limitations.

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

This paper proposes QD-Rec, a quality-diversity method for open-ended recommendation in decision-making.