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Multimodal Recommendation

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.

Papers

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Showing 1–50 of 59 papers

TitleStatusHype
Modality-Independent Graph Neural Networks with Global Transformers for Multimodal RecommendationCode2
A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future DirectionsCode2
Harnessing Multimodal Large Language Models for Multimodal Sequential RecommendationCode1
AlignRec: Aligning and Training in Multimodal RecommendationsCode1
COHESION: Composite Graph Convolutional Network with Dual-Stage Fusion for Multimodal RecommendationCode1
Train Once, Deploy Anywhere: Matryoshka Representation Learning for Multimodal RecommendationCode1
LGMRec: Local and Global Graph Learning for Multimodal RecommendationCode1
LightGT: A Light Graph Transformer for Multimedia RecommendationCode1
Disentangled Graph Variational Auto-Encoder for Multimodal Recommendation with InterpretabilityCode1
MENTOR: Multi-level Self-supervised Learning for Multimodal RecommendationCode1
Mining Latent Structures for Multimedia RecommendationCode1
Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local MinimaCode1
Modality-Balanced Learning for Multimedia RecommendationCode1
A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal RecommendationCode1
Spectrum-based Modality Representation Fusion Graph Convolutional Network for Multimodal RecommendationCode1
Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPsCode1
Ducho 2.0: Towards a More Up-to-Date Unified Framework for the Extraction of Multimodal Features in RecommendationCode1
Ducho: A Unified Framework for the Extraction of Multimodal Features in RecommendationCode1
Quadratic Interest Network for Multimodal Click-Through Rate PredictionCode1
End-to-end training of Multimodal Model and ranking ModelCode1
Enhancing Dyadic Relations with Homogeneous Graphs for Multimodal RecommendationCode1
Generating with Fairness: A Modality-Diffused Counterfactual Framework for Incomplete Multimodal RecommendationsCode1
Causality-Inspired Fair Representation Learning for Multimodal RecommendationCode1
GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal RecommendationCode1
X-Reflect: Cross-Reflection Prompting for Multimodal Recommendation—0
A Survey on Large Language Models in Multimodal Recommender Systems—0
ATFLRec: A Multimodal Recommender System with Audio-Text Fusion and Low-Rank Adaptation via Instruction-Tuned Large Language Model—0
Attention-guided Multi-step Fusion: A Hierarchical Fusion Network for Multimodal Recommendation—0
Attribute-driven Disentangled Representation Learning for Multimodal Recommendation—0
Bridging Domain Gaps between Pretrained Multimodal Models and Recommendations—0
Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach—0
Don't Lose Yourself: Boosting Multimodal Recommendation via Reducing Node-neighbor Discrepancy in Graph Convolutional Network—0
DREAM: A Dual Representation Learning Model for Multimodal Recommendation—0
HistLLM: A Unified Framework for LLM-Based Multimodal Recommendation with User History Encoding and Compression—0
ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation—0
Knowledge Soft Integration for Multimodal Recommendation—0
Learning ID-free Item Representation with Token Crossing for Multimodal Recommendation—0
MDVT: Enhancing Multimodal Recommendation with Model-Agnostic Multimodal-Driven Virtual Triplets—0
MMGRec: Multimodal Generative Recommendation with Transformer Model—0
Multi-Modal Hypergraph Enhanced LLM Learning for Recommendation—0
Multimodal Point-of-Interest Recommendation—0
Multimodal Pretraining and Generation for Recommendation: A Tutorial—0
Multimodal Recommendation Dialog with Subjective Preference: A New Challenge and Benchmark—0
Navigating the Future of Federated Recommendation Systems with Foundation Models—0
Dynamic Fusion Strategies for Federated Multimodal Recommendations—0
Rec-GPT4V: Multimodal Recommendation with Large Vision-Language Models—0
SynerGraph: An Integrated Graph Convolution Network for Multimodal Recommendation—0
Training-Free Graph Filtering via Multimodal Feature Refinement for Extremely Fast Multimodal Recommendation—0
Modality Reliability Guided Multimodal Recommendation—0
Multimodal Graph Neural Network for Recommendation with Dynamic De-redundancy and Modality-Guided Feature De-noisy—0
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