SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 26–50 of 59 papers

TitleStatusHype
A Survey on Large Language Models in Multimodal Recommender Systems—0
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
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
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
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
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
X-Reflect: Cross-Reflection Prompting for Multimodal Recommendation—0
Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation?Code0
SynerGraph: An Integrated Graph Convolution Network for Multimodal Recommendation—0
Navigating the Future of Federated Recommendation Systems with Foundation Models—0
Multimodal Pretraining and Generation for Recommendation: A Tutorial—0
MMGRec: Multimodal Generative Recommendation with Transformer Model—0
DREAM: A Dual Representation Learning Model for Multimodal Recommendation—0
Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach—0
Show:102550
← PrevPage 2 of 3Next →

No leaderboard results yet.