Papers Multimodal Recommendation
“Multimodal Recommendation” 태그가 달린 논문 85편 · 필터 해제
Latent-Aligned Reasoning for Multimodal Recommendation
Multimodal Vision-Language Models (VLMs) have demonstrated remarkable capabilities in cross-modal understanding, yet a fundamental challenge persists when applying them to recommendation: as representations propagate thr…
Multimodal RecommendationContrastive LearningMURAL: Multimodal Uncertainty-aware Recommendation via Adaptive edge Learning
Multimodal Graph Neural Networks have become standard for recommendation by augmenting sparse interaction data with content features. Yet current architectures face two bottlenecks: structural rigidity, from a reliance o…
Multimodal RecommendationPOI Recommendation with LLM-Augmented Multi-Graph Learning and Contrastive Alignment
Point-of-interest (POI) recommendation models based on graph neural networks achieve strong performance by propagating collaborative signals over user-item interactions, yet they struggle with the cold-start problem, whe…
Multimodal RecommendationCollaborative FilteringGraph Neural NetworkContrastive LearningWhat to Edit Next: Visually Aligned Image-Editing Follow-Up Suggestions in Conversational Systems
Conversational assistants increasingly recommend follow-up edits to help users continue a task. Existing systems primarily target text-only interactions, leaving image-creation conversations underexplored. In image-creat…
Multimodal RecommendationReinforcement LearningReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation
Recent advances in multimodal recommenders excel at feature fusion but remain opaque and inefficient decision-makers, lacking explicit reasoning and self-awareness of uncertainty. We introduce ReasonRec, a reasoning-augm…
Multimodal RecommendationTimeMM: Time-as-Operator Spectral Filtering for Dynamic Multimodal Recommendation
Multimodal recommendation improves user modeling by integrating collaborative signals with heterogeneous item content. In real applications, user interests evolve over time and exhibit nonstationary dynamics, where diffe…
Multimodal RecommendationTRU: Targeted Reverse Update for Efficient Multimodal Recommendation Unlearning
Multimodal recommendation systems (MRS) jointly model user-item interaction graphs and rich item content, but this tight coupling makes user data difficult to remove once learned. Approximate machine unlearning offers an…
Multimodal RecommendationVLM4Rec: Multimodal Semantic Representation for Recommendation with Large Vision-Language Models
Multimodal recommendation is commonly framed as a feature fusion problem, where textual and visual signals are combined to better model user preference. However, the effectiveness of multimodal recommendation may depend …
Multimodal RecommendationAnchored Alignment: Preventing Positional Collapse in Multimodal Recommender Systems
Multimodal recommender systems (MMRS) leverage images, text, and interaction signals to enrich item representations. However, recent alignment based MMRSs that enforce a unified embedding space often blur modality specif…
Multimodal RecommendationRepresentation LearningGeMi: A Graph-based, Multimodal Recommendation System for Narrative Scroll Paintings
Recommendation Systems are effective in managing the ever-increasing amount of multimodal data available today and help users discover interesting new items. These systems can handle various media types such as images, t…
Multimodal RecommendationRecommendation SystemsGraph Neural NetworkModality-Guided Mixture of Graph Experts with Entropy-Triggered Routing for Multimodal Recommendation
Multimodal recommendation enhances ranking by integrating user-item interactions with item content, which is particularly effective under sparse feedback and long-tail distributions. However, multimodal signals are inher…
Multimodal RecommendationGraph LearningDReX: An Explainable Deep Learning-based Multimodal Recommendation Framework
Multimodal recommender systems leverage diverse data sources, such as user interactions, content features, and contextual information, to address challenges like cold-start and data sparsity. However, existing methods of…
Multimodal RecommendationMoToRec: Sparse-Regularized Multimodal Tokenization for Cold-Start Recommendation
Graph neural networks (GNNs) have revolutionized recommender systems by effectively modeling complex user-item interactions, yet data sparsity and the item cold-start problem significantly impair performance, particularl…
Multimodal RecommendationLLM-driven Multimodal Recommendation
As a paradigm that delves into the deep seated drivers of user behavior, motivation-based recommendation systems have emerged as a prominent research direction in the field of personalized information retrieval. Unlike t…
Multimodal RecommendationRecommendation SystemsInformation RetrievalRecGOAT: Graph Optimal Adaptive Transport for LLM-Enhanced Multimodal Recommendation with Dual Semantic Alignment
Integrating large language model (LLM) representations into multimodal recommendation has shown promise, yet a fundamental challenge remains largely overlooked: the semantic heterogeneity between generative LM representa…
Multimodal RecommendationRecommendation SystemsContrastive LearningCross-Modal Attention Network with Dual Graph Learning in Multimodal Recommendation
Multimedia recommendation systems leverage user-item interactions and multimodal information to capture user preferences, enabling more accurate and personalized recommendations. Despite notable advancements, existing ap…
Multimodal RecommendationComputational EfficiencyRecommendation SystemsContrastive LearningQ-BERT4Rec: Quantized Semantic-ID Representation Learning for Multimodal Recommendation
Sequential recommendation plays a critical role in modern online platforms such as e-commerce, advertising, and content streaming, where accurately predicting users' next interactions is essential for personalization. Re…
Sequential RecommendationMultimodal RecommendationRepresentation LearningStructured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation
Multimodal recommendation aims to integrate collaborative signals with heterogeneous content such as visual and textual information, but remains challenged by modality-specific noise, semantic inconsistency, and unstable…
Multimodal RecommendationPolyRecommender: A Multimodal Recommendation System for Polymer Discovery
We introduce PolyRecommender, a multimodal discovery framework that integrates chemical language representations from PolyBERT with molecular graph-based representations from a graph encoder. The system first retrieves c…
Multimodal RecommendationStructurally Refined Graph Transformer for Multimodal Recommendation
Multimodal recommendation systems utilize various types of information, including images and text, to enhance the effectiveness of recommendations. The key challenge is predicting user purchasing behavior from the availa…
Multimodal Recommendation