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Multimodal Recommendation

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The multimodal recommendation task involves developing systems that leverage and integrate multiple types of data—such as text, images, audio, and user interactions—to predict and suggest items that align with a user's preferences. Unlike traditional recommendation approaches that rely on a single data modality, multimodal recommendation harnesses the diverse information from various sources to create richer and more nuanced representations of both users and items. This integration enables the system to understand and capture complex relationships and attributes across different data types, thereby enhancing the accuracy and relevance of the recommendations. The primary goal is to provide personalized suggestions by effectively merging and processing heterogeneous data to better match users with items they are likely to engage with or find valuable.

논문

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Multimodal Recommendation

논문

MDVT: Enhancing Multimodal Recommendation with Model-Agnostic Multimodal-Driven Virtual Triplets

A Survey on Large Language Models in Multimodal Recommender Systems

Quadratic Interest Network for Multimodal Click-Through Rate Prediction

Modality Reliability Guided Multimodal Recommendation

HistLLM: A Unified Framework for LLM-Based Multimodal Recommendation with User History Encoding and Compression

Multi-Modal Hypergraph Enhanced LLM Learning for Recommendation

COHESION: Composite Graph Convolutional Network with Dual-Stage Fusion for Multimodal Recommendation

Training-Free Graph Filtering via Multimodal Feature Refinement for Extremely Fast Multimodal Recommendation

Bridging Domain Gaps between Pretrained Multimodal Models and Recommendations

Collaborative Filtering Meets Spectrum Shift: Connecting User-Item Interaction with Graph-Structured Side Information

Generating with Fairness: A Modality-Diffused Counterfactual Framework for Incomplete Multimodal Recommendations

Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video Recommendation

Don't Lose Yourself: Boosting Multimodal Recommendation via Reducing Node-neighbor Discrepancy in Graph Convolutional Network

Spectrum-based Modality Representation Fusion Graph Convolutional Network for Multimodal Recommendation

Modality-Independent Graph Neural Networks with Global Transformers for Multimodal Recommendation

STAIR: Manipulating Collaborative and Multimodal Information for E-Commerce Recommendation

Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPs

Multimodal Graph Neural Network for Recommendation with Dynamic De-redundancy and Modality-Guided Feature De-noisy

Learning ID-free Item Representation with Token Crossing for Multimodal Recommendation

Dynamic Fusion Strategies for Federated Multimodal Recommendations

Multimodal Point-of-Interest Recommendation

A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios

Train Once, Deploy Anywhere: Matryoshka Representation Learning for Multimodal Recommendation

Ducho meets Elliot: Large-scale Benchmarks for Multimodal Recommendation

ATFLRec: A Multimodal Recommender System with Audio-Text Fusion and Low-Rank Adaptation via Instruction-Tuned Large Language Model

X-Reflect: Cross-Reflection Prompting for Multimodal Recommendation

Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation?

Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation

Modality-Balanced Learning for Multimedia Recommendation

GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal Recommendation

SynerGraph: An Integrated Graph Convolution Network for Multimodal Recommendation

Navigating the Future of Federated Recommendation Systems with Foundation Models

Multimodal Pretraining and Generation for Recommendation: A Tutorial

MMGRec: Multimodal Generative Recommendation with Transformer Model

DREAM: A Dual Representation Learning Model for Multimodal Recommendation

End-to-end training of Multimodal Model and ranking Model