Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Collaborative Filtering Through Weighted Similarities of User and Item Embeddings
In recent years, neural networks and other complex models have dominated recommender systems, often setting new benchmarks for state-of-the-art performance. Yet, despite these advancements, award-winning research has dem…
Computational EfficiencyCollaborative FilteringDual-Enhancement Product Bundling: Bridging Interactive Graph and Large Language Model
Product bundling boosts e-commerce revenue by recommending complementary item combinations. However, existing methods face two critical challenges: (1) collaborative filtering approaches struggle with cold-start items ow…
Collaborative FilteringGraph LearningClusterRAG: Cluster-Based Collaborative Filtering for Personalized Retrieval-Augmented Generation
Personalized Retrieval-Augmented Generation (RAG) relies on accurately selecting user-relevant documents. In practice, existing RAG approaches often suffer from high retrieval costs and overlook that collaborative signal…
Collaborative FilteringFlowAdam: Implicit Regularization via Geometry-Aware Soft Momentum Injection
Adaptive moment methods such as Adam use a diagonal, coordinate-wise preconditioner based on exponential moving averages of squared gradients. This diagonal scaling is coordinate-system dependent and can struggle with de…
Collaborative FilteringA Logical-Rule Autoencoder for Interpretable Recommendations
Most deep learning recommendation models operate as black boxes, relying on latent representations that obscure their decision process. This lack of intrinsic interpretability raises concerns in applications that require…
Collaborative FilteringLLMAR: A Tuning-Free Recommendation Framework for Sparse and Text-Rich Industrial Domains
Industrial B2B applications (e.g., construction site risk prediction, material procurement) face extreme data sparsity yet feature rich textual interactions. In such environments, traditional ID-based collaborative filte…
Collaborative FilteringPseudo Label NCF for Sparse OHC Recommendation: Dual Representation Learning and the Separability Accuracy Trade off
Online Health Communities connect patients for peer support, but users face a discovery challenge when they have minimal prior interactions to guide personalization. We study recommendation under extreme interaction spar…
Collaborative FilteringRepresentation LearningVLM2Rec: Resolving Modality Collapse in Vision-Language Model Embedders for Multimodal Sequential Recommendation
Sequential Recommendation (SR) in multimodal settings typically relies on small frozen pretrained encoders, which limits semantic capacity and prevents Collaborative Filtering (CF) signals from being fully integrated int…
Sequential RecommendationCollaborative FilteringContrastive LearningRaDAR: Relation-aware Diffusion-Asymmetric Graph Contrastive Learning for Recommendation
Collaborative filtering (CF) recommendation has been significantly advanced by integrating Graph Neural Networks (GNNs) and Graph Contrastive Learning (GCL). However, (i) random edge perturbations often distort critical …
Collaborative FilteringRecommendation SystemsContrastive LearningThe Confidence Gate Theorem: When Should Ranked Decision Systems Abstain?
Ranked decision systems -- recommenders, ad auctions, clinical triage queues -- must decide when to intervene in ranked outputs and when to abstain. We study when confidence-based abstention monotonically improves decisi…
Collaborative FilteringIntent DetectionTFPS: A Temporal Filtration-enhanced Positive Sample Set Construction Method for Implicit Collaborative Filtering
The negative sampling strategy can effectively train collaborative filtering (CF) recommendation models based on implicit feedback by constructing positive and negative samples. However, existing methods primarily optimi…
Collaborative FilteringE-MMKGR: A Unified Multimodal Knowledge Graph Framework for E-commerce Applications
Multimodal recommender systems (MMRSs) enhance collaborative filtering by leveraging item-side modalities, but their reliance on a fixed set of modalities and task-specific objectives limits both modality extensibility a…
Collaborative FilteringA Counterfactual Approach for Addressing Individual User Unfairness in Collaborative Recommender System
Recommender Systems (RSs) are exploited by various business enterprises to suggest their products (items) to consumers (users). Collaborative filtering (CF) is a widely used variant of RSs which learns hidden patterns fr…
Collaborative FilteringMulti-Armed BanditsImproving LLM-based Recommendation with Self-Hard Negatives from Intermediate Layers
Large language models (LLMs) have shown great promise in recommender systems, where supervised fine-tuning (SFT) is commonly used for adaptation. Subsequent studies further introduce preference learning to incorporate ne…
Collaborative FilteringRevisiting Content-Based Music Recommendation: Efficient Feature Aggregation from Large-Scale Music Models
Music Recommendation Systems (MRSs) are a cornerstone of modern streaming platforms. Existing recommendation models, spanning both recall and ranking stages, predominantly rely on collaborative filtering, which fails to …
Collaborative FilteringRecommendation SystemsAMEM4Rec: Leveraging Cross-User Similarity for Memory Evolution in Agentic LLM Recommenders
Agentic systems powered by Large Language Models (LLMs) have shown strong potential in recommender systems but remain hindered by several challenges. Fine-tuning LLMs is parameter-inefficient, and prompt-based agentic re…
Collaborative FilteringRecommendation SystemsMultimodal Enhancement of Sequential Recommendation
We propose a novel recommender framework, MuSTRec (Multimodal and Sequential Transformer-based Recommendation), that unifies multimodal and sequential recommendation paradigms. MuSTRec captures cross-item similarities an…
Sequential RecommendationCollaborative FilteringAspect-Aware MOOC Recommendation in a Heterogeneous Network
MOOC recommendation systems have received increasing attention to help learners navigate and select preferred learning content. Traditional methods such as collaborative filtering and content-based filtering suffer from …
Collaborative FilteringRecommendation SystemsGraph Neural NetworkReasoning-guided Collaborative Filtering with Language Models for Explainable Recommendation
Large Language Models (LLMs) exhibit potential for explainable recommendation systems but overlook collaborative signals, while prevailing methods treat recommendation and explanation as separate tasks, resulting in a me…
Sequential RecommendationCollaborative FilteringRepresentation LearningRecommendation SystemsHELM: A Human-Centered Evaluation Framework for LLM-Powered Recommender Systems
The integration of Large Language Models (LLMs) into recommendation systems has introduced unprecedented capabilities for natural language understanding, explanation generation, and conversational interactions. However, …
Natural Language UnderstandingCollaborative FilteringExplanation GenerationRecommendation Systems