Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Epistemic Uncertainty-aware Recommendation Systems via Bayesian Deep Ensemble Learning
Recommending items to users has long been a fundamental task, and studies have tried to improve it ever since. Most well-known models commonly employ representation learning to map users and items into a unified embeddin…
Collaborative FilteringEnsemble LearningRecommendation SystemsRepresentation LearningUnveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering
Personalized recommendation is widely used in the web applications, and graph contrastive learning (GCL) has gradually become a dominant approach in recommender systems, primarily due to its ability to extract self-super…
Collaborative FilteringContrastive LearningData AugmentationMisconceptions+1Enhancing LLM-based Recommendation through Semantic-Aligned Collaborative Knowledge
Large Language Models (LLMs) demonstrate remarkable capabilities in leveraging comprehensive world knowledge and sophisticated reasoning mechanisms for recommendation tasks. However, a notable limitation lies in their in…
Collaborative FilteringTransfer LearningWorld KnowledgeRetrieval Augmented Generation with Collaborative Filtering for Personalized Text Generation
Recently, the personalization of Large Language Models (LLMs) to generate content that aligns with individual user preferences has garnered widespread attention. Personalized Retrieval-Augmented Generation (RAG), which r…
Collaborative FilteringContrastive LearningRAGRecommendation Systems+4Why is Normalization Necessary for Linear Recommenders?
Despite their simplicity, linear autoencoder (LAE)-based models have shown comparable or even better performance with faster inference speed than neural recommender models. However, LAEs face two critical challenges: (i)…
Collaborative FilteringRecommendation SystemsGraph-based Diffusion Model for Collaborative Filtering
Recently, diffusion-based recommendation methods have achieved impressive results. However, existing approaches predominantly treat each user's historical interactions as independent training samples, overlooking the pot…
Collaborative FilteringmodelCan LLM-Driven Hard Negative Sampling Empower Collaborative Filtering? Findings and Potentials
Hard negative samples can accelerate model convergence and optimize decision boundaries, which is key to improving the performance of recommender systems. Although large language models (LLMs) possess strong semantic und…
Collaborative FilteringProfile GenerationRecommendation SystemsSqueeze and Excitation: A Weighted Graph Contrastive Learning for Collaborative Filtering
Contrastive Learning (CL) has recently emerged as a powerful technique in recommendation systems, particularly for its capability to harness self-supervised signals from perturbed views to mitigate the persistent challen…
Collaborative FilteringContrastive LearningRecommendation SystemsRepresentation LearningEfficient Evaluation of Large Language Models via Collaborative Filtering
With the development of Large Language Models (LLMs), numerous benchmarks have been proposed to measure and compare the capabilities of different LLMs. However, evaluating LLMs is costly due to the large number of test i…
Collaborative FilteringRecommendation SystemsShallow AutoEncoding Recommender with Cold Start Handling via Side Features
User and item cold starts present significant challenges in industrial applications of recommendation systems. Supplementing user-item interaction data with metadata is a common solution-but often at the cost of introduc…
Collaborative FilteringRecommendation SystemsGraphs are everywhere -- Psst! In Music Recommendation too
In recent years, graphs have gained prominence across various domains, especially in recommendation systems. Within the realm of music recommendation, graphs play a crucial role in enhancing genre-based recommendations b…
Collaborative FilteringMusic RecommendationRecommendation SystemsDrunkAgent: Stealthy Memory Corruption in LLM-Powered Recommender Agents
Large language model (LLM)-powered agents are increasingly used in recommender systems (RSs) to achieve personalized behavior modeling, where the memory mechanism plays a pivotal role in enabling the agents to autonomous…
Collaborative FilteringLarge Language ModelRecommendation SystemsCombiGCN: An effective GCN model for Recommender System
Graph Neural Networks (GNNs) have opened up a potential line of research for collaborative filtering (CF). The key power of GNNs is based on injecting collaborative signal into user and item embeddings which will contain…
Collaborative FilteringRecommendation SystemsImprovement Graph Convolution Collaborative Filtering with Weighted addition input
Graph Neural Networks have been extensively applied in the field of machine learning to find features of graphs, and recommendation systems are no exception. The ratings of users on considered items can be represented by…
Collaborative FilteringRecommendation SystemsBeLightRec: A lightweight recommender system enhanced with BERT
The trend of data mining using deep learning models on graph neural networks has proven effective in identifying object features through signal encoders and decoders, particularly in recommendation systems utilizing coll…
Collaborative FilteringRecommendation SystemsSemantic SimilaritySemantic Textual Similarity+3Beyond Relevance: An Adaptive Exploration-Based Framework for Personalized Recommendations
Recommender systems must balance personalization, diversity, and robustness to cold-start scenarios to remain effective in dynamic content environments. This paper introduces an adaptive, exploration-based recommendation…
Collaborative FilteringDiversityRecommendation SystemsSentenceDiffusion-augmented Graph Contrastive Learning for Collaborative Filter
Graph-based collaborative filtering has been established as a prominent approach in recommendation systems, leveraging the inherent graph topology of user-item interactions to model high-order connectivity patterns and e…
Collaborative FilteringContrastive LearningData AugmentationRecommendation Systems+1Variational Bayesian Personalized Ranking
Recommendation systems have found extensive applications across diverse domains. However, the training data available typically comprises implicit feedback, manifested as user clicks and purchase behaviors, rather than e…
Collaborative FilteringContrastive LearningRecommendation SystemsHarmonizing Large Language Models with Collaborative Behavioral Signals for Conversational Recommendation
Conversational recommendation frameworks have gained prominence as a dynamic paradigm for delivering personalized suggestions via interactive dialogues. The incorporation of advanced language understanding techniques has…
Collaborative FilteringConversational RecommendationWeighted Tensor Decompositions for Context-aware Collaborative Filtering
Over recent years it has become well accepted that user interest is not static or immutable. There are a variety of contextual factors, such as time of day, the weather or the user's mood, that influence the current inte…
Collaborative FilteringMissing ValuesTensor Decomposition