Tags: Generative Recommendation Systems - Paper Library

OxygenREC: An Instruction-Following Generative Framework for E-commerce Recommendation

Published:12/27/2025

Generative Recommendation SystemsMulti-Stage Optimization ObjectivesInstruction-Guided RetrievalDeep Reasoning CapabilitiesControllable Instructions from Scenario Information

OxygenREC is an ecommerce recommendation system utilizing a FastSlow Thinking architecture for deep reasoning, addressing inconsistencies in multistage optimization and independent training across scenarios, while enhancing recommendation quality with a semantic alignment mech

DiffuRec: A Diffusion Model for Sequential Recommendation

Published:4/3/2023

Diffusion Model for Sequential RecommendationUncertainty InjectionDistribution-Based Item RepresentationSequential Recommender SystemsGenerative Recommendation Systems

This paper introduces DiffuRec, the first diffusion model for sequential recommendation, representing item embeddings as distributions to better reflect multiple user interests and diverse item features. The method leverages noise addition for uncertainty injection and reconstruc

Recommender Systems with Generative Retrieval

Published:5/9/2023

Generative Recommendation SystemsSemantic ID-based Recommendation ModelTransformer Sequence-to-Sequence ModelApproximate Nearest Neighbor SearchUser Behavior Prediction

This paper introduces a novel generative retrieval method using autoregressive decoding of Semantic IDs to enhance recommender system performance. A Transformerbased model effectively predicts the next item a user will interact with. Experiments show substantial improvements ove

Learning Decomposed Contextual Token Representations from Pretrained and Collaborative Signals for Generative Recommendation

Published:8/23/2025

Generative Recommendation SystemsContextual Token Representation LearningLarge Language Model OptimizationSequence-to-Sequence ModelingUser Interaction Modeling

The DECOR framework addresses limitations in generative recommenders, enhancing token adaptability while preserving pretrained semantics. It employs contextualized token composition and decomposed embedding fusion, demonstrating superior performance on real datasets compared to s

HiD-VAE: Interpretable Generative Recommendation via Hierarchical and Disentangled Semantic IDs

Published:8/7/2025

Generative Recommendation SystemsHierarchical Semantic IDsInterpretable Generative RecommendationDisentangled Representation LearningUniqueness Loss Mechanism

HiDVAE is a proposed framework that enhances generative recommendation by learning hierarchically disentangled item representations, addressing traditional methods' flatness and entanglement issues, thereby improving recommendation accuracy and diversity.

TokenRec: Learning to Tokenize ID for LLM-based Generative Recommendation

Published:6/15/2024

LLM-based Recommendation SystemsGenerative Recommendation SystemsUser-Item ID TokenizationMasked Vector-Quantized TokenizerCapturing High-Order Collaborative Knowledge for LLMs

TokenRec is introduced as a novel framework for enhancing LLMbased recommendation systems by effectively tokenizing user and item IDs. Featuring the Masked VectorQuantized Tokenizer and generative retrieval, it captures highorder collaborative knowledge, improving recommendati

Inductive Generative Recommendation via Retrieval-based Speculation

Published:10/4/2024

Generative Recommendation SystemsTraining-Free Acceleration MethodsOnline Recommendation System OptimizationSequential Recommender SystemsImage Generation

The paper introduces , a retrievalbased inductive generative recommendation framework that addresses the limitations of generative models in recommending unseen items by utilizing a drafter model for candidate generation and a generative model for verification, enhancing

Pre-training Generative Recommender with Multi-Identifier Item Tokenization

Published:4/6/2025

Generative Recommendation SystemsMulti-Identifier Item TokenizationCurriculum Recommender Pre-TrainingRQ-VAE as TokenizerLow-Frequency Item Semantic Modeling

The MTGRec framework enhances generative recommender pretraining through multiidentifier item tokenization, using RQVAE for multiple identifier association and a curriculum learning scheme to improve semantic modeling for lowfrequency items and token diversity.

A Survey of Generative Recommendation from a Tri-Decoupled Perspective: Tokenization, Architecture, and Optimization

Generative Recommendation SystemsModel Optimization MethodsRecommendation System ArchitectureTokenization Techniques

This survey explores three key aspects of generative recommendation systems: tokenization, architecture, and optimization, highlighting how generative methods mitigate error propagation, enhance hardware utilization, and extend beyond local user behavior, while tracing the evolut

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Masked Diffusion for Generative Recommendation

Published:11/28/2025

Generative Recommendation SystemsMasked Diffusion ModelSemantic ID ModelingSequential Recommender SystemsAutoregressive Modeling

This paper introduces Masked Diffusion for Generative Recommendation (MADRec), which models user interaction sequences using discrete masking noise, outperforming traditional autoregressive models in efficiency and performance, particularly in datarestricted and coarsegrained r

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CoFiRec: Coarse-to-Fine Tokenization for Generative Recommendation

Published:11/28/2025

Generative Recommendation SystemsFine-grained User Preference ModelingAutoregressive Recommendation GenerationCoarse-to-Fine Semantic HierarchyEvaluation on Public Benchmarks

The CoFiRec framework enhances generative recommendation by incorporating a coarsetofine semantic hierarchy into the tokenization process, allowing for better modeling of user intent. Experiments demonstrate its superiority over existing methods across multiple benchmarks.

UNGER: Generative Recommendation with A Unified Code via Semantic and Collaborative Integration

Published:10/28/2025

Generative Recommendation SystemsKnowledge Graph-based RecommendationPersonalized Recommendation SystemMultimodal Recommendation SystemsOnline Recommendation System Optimization

The paper introduces UNGER, a generative recommendation approach that integrates semantic and collaborative information into a unified code to reduce storage and inference costs. Utilizing a twophase framework for effective code construction, it demonstrates significant improvem

Killing Two Birds with One Stone: Unifying Retrieval and Ranking with a Single Generative Recommendation Model

Published:4/23/2025

Generative Recommendation SystemsUnified Generative Recommendation FrameworkRetrieval and Ranking in Recommendation SystemsInformation Sharing and OptimizationDynamic Balancing Optimization Mechanism

The study introduces the Unified Generative Recommendation Framework (UniGRF) to unify retrieval and ranking stages in recommendation systems, enhancing performance through information sharing and dynamic optimization, outperforming existing models in extensive experiments.

Semantics Meet Signals: Dual Codebook Representationl Learning for Generative Recommendation

Published:11/15/2025

Generative Recommendation SystemsUniversal Recommendation SystemsDual Codebook Representation LearningBalancing Collaborative Filtering and Semantic UnderstandingFlexCode Framework

This paper introduces FlexCode, a dual codebook framework that balances collaborative filtering and semantic understanding in generative recommendation systems, enhancing performance with adaptive token allocation and a lightweight mixtureofexperts model, outperforming strong b

Recommender Systems in the Era of Large Language Models (LLMs)

Published:7/5/2023

LLM-based Recommendation SystemsLarge Language Model Fine-TuningGenerative Recommendation SystemsPre-training and Fine-tuning in Recommender SystemsPrompting Methods for Large Language Models

This paper reviews techniques for enhancing recommender systems using Large Language Models (LLMs), focusing on pretraining, finetuning, and prompting. It highlights LLMs' potential in feature encoding and their future applications in recommender system research.

Multi-Aspect Cross-modal Quantization for Generative Recommendation

Published:11/19/2025

Generative Recommendation SystemsCross-modal QuantizationMultimodal Information IntegrationSemantic IDs LearningRecommendation Datasets

This paper introduces the MACRec model for generative recommendation, integrating multimodal information to improve semantic ID quality. It employs crossmodal quantization to reduce conflict rates and combines implicit and explicit alignments, enhancing the generative model's pe

LLaDA-Rec: Discrete Diffusion for Parallel Semantic ID Generation in Generative Recommendation

Published:11/9/2025

Generative Recommendation SystemsDiscrete Diffusion FrameworkParallel Semantic ID GenerationBidirectional Attention MechanismAdaptive Sequence Generation

LLaDARec is a discrete diffusion framework for generative recommendation, addressing unidirectional constraints and error accumulation. By integrating bidirectional attention and adaptive generation order, it effectively models item dependencies, surpassing existing systems in r

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GPR: Towards a Generative Pre-trained One-Model Paradigm for Large-Scale Advertising Recommendation

Published:11/13/2025

Generative Recommendation SystemsAdvertising Recommendation OptimizationUnified Generative Model FrameworkMulti-Stage Joint Training StrategyHeterogeneous Hierarchical Decoder

This paper presents GPR (Generative Pretrained Recommender), a novel framework that redefines advertising recommendation as an endtoend generative task, overcoming issues of objective misalignment and error propagation, while enhancing semantic alignment and modeling consisten

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Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations

Published:3/4/2025

Sparse-Dense Recommendation ModelGenerative Recommendation SystemsCascaded Sparse-Dense RepresentationsUser Interaction Sequence ModelingOnline Recommendation System Optimization

The study introduces the COBRA framework, which integrates sparse semantic IDs and dense vectors through alternating generation. This endtoend training enhances dynamic optimization of representations, effectively capturing semantic and collaborative insights from useritem int

A Survey on Generative Recommendation: Data, Model, and Tasks

Published:10/31/2025

Generative Recommendation SystemsLarge Language Model Fine-TuningDiffusion ModelsMultimodal Large Language ModelLLM-based Recommendation Systems

This survey reviews generative recommendation via a unified framework, analyzing data augmentation, model alignment, and task design, highlighting innovations in large language and diffusion models that enable knowledge integration, natural language understanding, and personalize

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