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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 26–50 of 59 papers

TitleStatusHype
X-Reflect: Cross-Reflection Prompting for Multimodal Recommendation—0
Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation?Code0
Harnessing Multimodal Large Language Models for Multimodal Sequential RecommendationCode1
Modality-Balanced Learning for Multimedia RecommendationCode1
GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal RecommendationCode1
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
End-to-end training of Multimodal Model and ranking ModelCode1
Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach—0
AlignRec: Aligning and Training in Multimodal RecommendationsCode1
Ducho 2.0: Towards a More Up-to-Date Unified Framework for the Extraction of Multimodal Features in RecommendationCode1
MENTOR: Multi-level Self-supervised Learning for Multimodal RecommendationCode1
Disentangled Graph Variational Auto-Encoder for Multimodal Recommendation with InterpretabilityCode1
Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local MinimaCode1
Rec-GPT4V: Multimodal Recommendation with Large Vision-Language Models—0
LGMRec: Local and Global Graph Learning for Multimodal RecommendationCode1
Attribute-driven Disentangled Representation Learning for Multimodal Recommendation—0
ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation—0
Causality-Inspired Fair Representation Learning for Multimodal RecommendationCode1
Semantic-Guided Feature Distillation for Multimodal RecommendationCode0
LightGT: A Light Graph Transformer for Multimedia RecommendationCode1
Ducho: A Unified Framework for the Extraction of Multimodal Features in RecommendationCode1
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