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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
Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation?Code0
MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-videoCode0
A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality ScenariosCode0
Semantic-Guided Feature Distillation for Multimodal RecommendationCode0
Ducho meets Elliot: Large-scale Benchmarks for Multimodal RecommendationCode0
Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video RecommendationCode0
Collaborative Filtering Meets Spectrum Shift: Connecting User-Item Interaction with Graph-Structured Side InformationCode0
STAIR: Manipulating Collaborative and Multimodal Information for E-Commerce RecommendationCode0
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
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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