Multi-modal Recommendation
3개 벤치마크 · 논문 34편 · 이 태스크의 논문 보기 →
Benchmarks
Most implemented
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
Multi-Modal Self-Supervised Learning for Recommendation
A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal Recommendation
Bootstrap Latent Representations for Multi-modal Recommendation
Papers
TimeRoute: Time-Aware Modality Routing and Diffusion for Multi-Modal Recommendation
Multi-modal recommenders fuse user-item interaction signals with item modalities such as text, images, and audio, but the usefulness of each drifts over time and at different rates. For example, around Valentine's Day, c…
Multi-modal RecommendationUser-Aware Conditional Generative Total Correlation Learning for Multi-Modal Recommendation
Multi-modal recommendation (MMR) enriches item representations by introducing item content, e.g., visual and textual descriptions, to improve upon interaction-only recommenders. The success of MMR hinges on aligning thes…
Multi-modal RecommendationHi-SAM: A Hierarchical Structure-Aware Multi-modal Framework for Large-Scale Recommendation
Multi-modal recommendation has gained traction as items possess rich attributes like text and images. Semantic ID-based approaches effectively discretize this information into compact tokens. However, two challenges pers…
Multi-modal RecommendationDiffusion-based Multi-modal Synergy Interest Network for Click-through Rate Prediction
In click-through rate prediction, click-through rate prediction is used to model users' interests. However, most of the existing CTR prediction methods are mainly based on the ID modality. As a result, they are unable to…
Click-Through Rate PredictionMulti-modal RecommendationRAG-VisualRec: An Open Resource for Vision- and Text-Enhanced Retrieval-Augmented Generation in Recommendation
This paper addresses the challenge of developing multimodal recommender systems for the movie domain, where limited metadata (e.g., title, genre) often hinders the generation of robust recommendations. We introduce a res…
Collaborative FilteringData AugmentationMulti-modal RecommendationRAG+3Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration
The surge in multimedia content has led to the development of Multi-Modal Recommender Systems (MMRecs), which use diverse modalities such as text, images, videos, and audio for more personalized recommendations. However,…
DenoisingKnowledge DistillationMulti-modal RecommendationRecommendation Systems