Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Why not Collaborative Filtering in Dual View? Bridging Sparse and Dense Models
Collaborative Filtering (CF) remains the cornerstone of modern recommender systems, with dense embedding--based methods dominating current practice. However, these approaches suffer from a critical limitation: our theore…
Collaborative FilteringMemRec: Collaborative Memory-Augmented Agentic Recommender System
The evolution of recommender systems has shifted from traditional collaborative filtering to LLM-based agentic systems, which rely on semantic user and item memories to make predictions. However, existing agents maintain…
Collaborative FilteringDo LLMs Benefit from User and Item Embeddings in Recommendation Tasks?
Large Language Models (LLMs) have emerged as promising recommendation systems, offering novel ways to model user preferences through generative approaches. However, many existing methods often rely solely on text semanti…
Collaborative FilteringRecommendation SystemsLatent Geometry of Taste: Scalable Low-Rank Matrix Factorization for Recommender Systems
Scalability and data sparsity remain critical bottlenecks for collaborative filtering on massive interaction datasets. This work investigates the latent geometry of user preferences using the MovieLens 32M dataset, imple…
Hyperparameter OptimizationCollaborative FilteringMergeRec: Model Merging for Data-Isolated Cross-Domain Sequential Recommendation
Modern recommender systems trained on domain-specific data often struggle to generalize across multiple domains. Cross-domain sequential recommendation has emerged as a promising research direction to address this challe…
Sequential RecommendationCollaborative FilteringA UCB Bandit Algorithm for General ML-Based Estimators
We present ML-UCB, a generalized upper confidence bound algorithm that integrates arbitrary machine learning models into multi-armed bandit frameworks. A fundamental challenge in deploying sophisticated ML models for seq…
Collaborative FilteringScienceDB AI: An LLM-Driven Agentic Recommender System for Large-Scale Scientific Data Sharing Services
The rapid growth of AI for Science (AI4S) has underscored the significance of scientific datasets, leading to the establishment of numerous national scientific data centers and sharing platforms. Despite this progress, e…
Collaborative FilteringGenZ: Foundational models as latent variable generators within traditional statistical models
We present GenZ, a hybrid model that bridges foundational models and statistical modeling through interpretable semantic features. While large language models possess broad domain knowledge, they often fail to capture da…
Collaborative FilteringDPSR: Differentially Private Sparse Reconstruction via Multi-Stage Denoising for Recommender Systems
Differential privacy (DP) has emerged as the gold standard for protecting user data in recommender systems, but existing privacy-preserving mechanisms face a fundamental challenge: the privacy-utility tradeoff inevitably…
Collaborative FilteringBERT and CNN integrated Neural Collaborative Filtering for Recommender Systems
Every day, a significant number of users visit the internet for different needs. The owners of a website generate profits from the user interaction with the contents or items of the website. A robust recommendation syste…
Collaborative FilteringRethinking Popularity Bias in Collaborative Filtering via Analytical Vector Decomposition
Popularity bias fundamentally undermines the personalization capabilities of collaborative filtering (CF) models, causing them to disproportionately recommend popular items while neglecting users' genuine preferences for…
Collaborative FilteringBenchmarking Deep Neural Networks for Modern Recommendation Systems
This paper presents a requirement-oriented benchmark of seven deep neural architectures, CNN, RNN, GNN, Autoencoder, Transformer, Neural Collaborative Filtering, and Siamese Networks, across three real-world datasets: Re…
Computational EfficiencyCollaborative FilteringRecommendation SystemsDLRREC: Denoising Latent Representations via Multi-Modal Knowledge Fusion in Deep Recommender Systems
Modern recommender systems struggle to effectively utilize the rich, yet high-dimensional and noisy, multi-modal features generated by Large Language Models (LLMs). Treating these features as static inputs decouples them…
Dimensionality ReductionCollaborative FilteringContrastive LearningPrivacy-Utility-Bias Trade-offs for Privacy-Preserving Recommender Systems
Recommender systems (RSs) output ranked lists of items, such as movies or restaurants, that users may find interesting, based on the user's past ratings and ratings from other users. RSs increasingly incorporate differen…
Collaborative FilteringCoFiRec: Coarse-to-Fine Tokenization for Generative Recommendation
In web environments, user preferences are often refined progressively as users move from browsing broad categories to exploring specific items. However, existing generative recommenders overlook this natural refinement p…
Collaborative FilteringTextBridgeGNN: Pre-training Graph Neural Network for Cross-Domain Recommendation via Text-Guided Transfer
Graph-based recommendation has achieved great success in recent years. The classical graph recommendation model utilizes ID embedding to store essential collaborative information. However, this ID-based paradigm faces ch…
Collaborative FilteringGraph Neural NetworkLLM Reasoning for Cold-Start Item Recommendation
Large Language Models (LLMs) have shown significant potential for improving recommendation systems through their inherent reasoning capabilities and extensive knowledge base. Yet, existing studies predominantly address w…
Collaborative FilteringReinforcement LearningRecommendation SystemsMulti-Agent Collaborative Filtering: Orchestrating Users and Items for Agentic Recommendations
Agentic recommendations cast recommenders as large language model (LLM) agents that can plan, reason, use tools, and interact with users of varying preferences in web applications. However, most existing agentic recommen…
Collaborative FilteringEffective Diversification of Multi-Carousel Book Recommendation
Using multiple carousels, lists that wrap around and can be scrolled, is the basis for offering content in most contemporary movie streaming platforms. Carousels allow for highlighting different aspects of users' taste, …
Collaborative FilteringLocal Collaborative Filtering: A Collaborative Filtering Method that Utilizes Local Similarities among Users
To leverage user behavior data from the Internet more effectively in recommender systems, this paper proposes a novel collaborative filtering (CF) method called Local Collaborative Filtering (LCF). LCF utilizes local sim…
Collaborative Filtering