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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 1–25 of 59 papers

TitleStatusHype
MDVT: Enhancing Multimodal Recommendation with Model-Agnostic Multimodal-Driven Virtual Triplets—0
A Survey on Large Language Models in Multimodal Recommender Systems—0
Quadratic Interest Network for Multimodal Click-Through Rate PredictionCode1
Modality Reliability Guided Multimodal Recommendation—0
HistLLM: A Unified Framework for LLM-Based Multimodal Recommendation with User History Encoding and Compression—0
Multi-Modal Hypergraph Enhanced LLM Learning for Recommendation—0
COHESION: Composite Graph Convolutional Network with Dual-Stage Fusion for Multimodal RecommendationCode1
Training-Free Graph Filtering via Multimodal Feature Refinement for Extremely Fast Multimodal Recommendation—0
Bridging Domain Gaps between Pretrained Multimodal Models and Recommendations—0
Collaborative Filtering Meets Spectrum Shift: Connecting User-Item Interaction with Graph-Structured Side InformationCode0
Generating with Fairness: A Modality-Diffused Counterfactual Framework for Incomplete Multimodal RecommendationsCode1
Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video RecommendationCode0
Don't Lose Yourself: Boosting Multimodal Recommendation via Reducing Node-neighbor Discrepancy in Graph Convolutional Network—0
Spectrum-based Modality Representation Fusion Graph Convolutional Network for Multimodal RecommendationCode1
Modality-Independent Graph Neural Networks with Global Transformers for Multimodal RecommendationCode2
Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPsCode1
STAIR: Manipulating Collaborative and Multimodal Information for E-Commerce RecommendationCode0
Multimodal Graph Neural Network for Recommendation with Dynamic De-redundancy and Modality-Guided Feature De-noisy—0
Learning ID-free Item Representation with Token Crossing for Multimodal Recommendation—0
Dynamic Fusion Strategies for Federated Multimodal Recommendations—0
Multimodal Point-of-Interest Recommendation—0
A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality ScenariosCode0
Train Once, Deploy Anywhere: Matryoshka Representation Learning for Multimodal RecommendationCode1
Ducho meets Elliot: Large-scale Benchmarks for Multimodal RecommendationCode0
ATFLRec: A Multimodal Recommender System with Audio-Text Fusion and Low-Rank Adaptation via Instruction-Tuned Large Language Model—0
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