Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Recovering Expert Critic-Sourced Network Adjacency between Musical Artists from Acoustic Distributions: A Construct-Validity Approach
Music recommendation relies primarily on two signals: user-item interactions, which fail in the cold-start regime, and intrinsic musical content, available for any recording. We argue that a third, largely untapped signa…
Collaborative FilteringAdaptive Item-based Collaborative Structures via Noise Rescheduling in Diffusion for Generative Recommendation
Discrete Diffusion Models (DDMs) have recently been introduced to recommendation systems, modeling user history as a token generation process via iterative denoising. However, while effective at capturing user-level sequ…
Collaborative FilteringRecommendation SystemsUniDot: A Unified Network for Sequence Modeling and Feature Interaction in Large-scale Recommendation
Industrial recommenders rely on two model families that have evolved largely independently: feature-interaction models over multi-field user/item features, and sequential models over user-behavior histories. Production s…
Collaborative FilteringPOI 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 LearningKairos: Numerically Robust News Recommendation under Item Cold-Start via Cholesky-based LinUCB
Algorithmic news personalization in regional markets often fails because modern deep learning models require massive interaction data while real-world news has a short Time-to-Live (TTL < 48 h) and shallow article pools.…
Representation LearningCollaborative FilteringRecommendation SystemsTailorMind: Towards Preference-Aligned Multimodal Content Generation
Personalized content systems depend on available UGC and struggle when suitable content is absent, delayed, or costly to create. Although multimodal generators can synthesize content on demand, how to translate behaviora…
Collaborative Filteringmultimodal generationMemory Is No Longer a Bottleneck: Memory-Efficient Graph Filtering for Scalable Collaborative Filtering
Graph convolutional networks (GCNs) have demonstrated significant success in capturing complex user-item relationships for collaborative filtering (CF). However, due to their reliance on extensive model training, trainin…
Computational EfficiencyCollaborative FilteringVCG: A Multimodal Retrieval Framework for E-Commerce Video Feeds under Extreme Cold-Start Conditions
The digital commerce landscape is shifting from static, search-driven catalogs to dynamic, immersive video feeds. This transition introduces an ``extreme cold-start'' problem: unlike traditional items, new short-form vid…
Collaborative FilteringMood-Aware Music Recommendation: Integrating User Affective Signals into Ranking Systems
Recommendation systems are essential in modern music streaming platforms due to the vast amount of available content. While collaborative filtering is widely used to suggest items based on the preferences of others with …
Collaborative FilteringRecommendation SystemsEmotion RecognitionBridging the Semantic-Collaborative Gap: An Asymmetric Graph Architecture for Cold-Start Item Recommendation
Collaborative filtering and graph-based recommendation models are highly effective because they leverage observed user interactions, but this dependence creates a fundamental cold-start challenge when newly added content…
Collaborative FilteringThe Ghost Annotator: a Framework to Explore Human Label Variation in Content Moderation through Conformal Prediction
Current research primarily focuses on model performance, while comparatively less attention has been devoted to uncertainty estimation, particularly in settings where LLMs are increasingly used to generate annotated data…
Collaborative FilteringActing on the Unseen: Communication-Free Collaborative Filtering for Decentralized Multi-Robot Task Allocation
Multi-robot task allocation usually assumes some combination of communication, known task models, or a coordinator. We study the opposite extreme, a regime common in practice but overlooked in theory, which we name Zero-…
Collaborative FilteringAn Interpretable CF-RL-TOPSIS Fusion Model for Skills-Aware Talent Recommendation
Effective skills-aware talent recommendation must balance behavioral transition patterns, trajectory-sensitive adaptation, and inspectable occupation-level criteria. Evidence from public benchmarks on how these signals i…
Collaborative FilteringBuilding a privacy-preserving Federated Recommender system for mobile devices
Serving personalized content on mobile devices has traditionally required pooling sensitive user data on centralized servers, a practice increasingly at odds with modern privacy expectations and geographical regulations.…
Human Activity RecognitionCollaborative FilteringFLUID: From Ephemeral IDs to Multimodal Semantic Codes for Industrial-Scale Livestreaming Recommendation
Modern recommender systems rely heavily on ID-based collaborative filtering: each item is represented by a unique ID embedding that accumulates collaborative signals from user interactions. Livestreaming recommendation, …
Collaborative FilteringContexting as Recommendation: Evolutionary Collaborative Filtering for Context Engineering
Large Language Models (LLMs) are highly sensitive to their input contexts, motivating the development of automated context engineering. However, existing methods predominantly treat this as a global search problem, seeki…
Collaborative FilteringDebiasing Message Passing to Mitigate Popularity Bias in GNN-based Collaborative Filtering
Collaborative filtering (CF) models based on graph neural networks (GNNs) achieve strong performance in recommender systems by propagating user-item signals over interaction graphs. However, they are highly susceptible t…
Collaborative FilteringDynamic Graph with Similarity-Aware Attention Graph Neural Network for Recommender Systems
Recommender systems are essential components of modern online platforms which presents personalized content in various domain. The traditional collaborative filtering methods depends on static user-item interaction graph…
Collaborative FilteringGraph Neural NetworkA Gated Hybrid Contrastive Collaborative Filtering Recommendation
Recommender systems increasingly incorporate textual reviews to enrich user and item representations. However, most review-aware models remain optimized for rating prediction rather than ranking quality. This misalignmen…
Collaborative FilteringContrastive LearningLearned Nonlocal Feature Matching and Filtering for RAW Image Denoising
Being one of the oldest and most basic problems in image processing, image denoising has seen a resurgence spurred by rapid advances in deep learning. Yet, most modern denoising architectures make limited use of the tech…
Collaborative FilteringImage Denoising