Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Countering Mainstream Bias via End-to-End Adaptive Local Learning
Collaborative filtering (CF) based recommendations suffer from mainstream bias -- where mainstream users are favored over niche users, leading to poor recommendation quality for many long-tail users. In this paper, we id…
Collaborative FilteringMixture-of-ExpertsA Strategy Transfer and Decision Support Approach for Epidemic Control in Experience Shortage Scenarios
Epidemic outbreaks can cause critical health concerns and severe global economic crises. For countries or regions with new infectious disease outbreaks, it is essential to generate preventive strategies by learning lesso…
Collaborative FilteringDecision MakingWasserstein Dependent Graph Attention Network for Collaborative Filtering with Uncertainty
Collaborative filtering (CF) is an essential technique in recommender systems that provides personalized recommendations by only leveraging user-item interactions. However, most CF methods represent users and items as fi…
Collaborative FilteringGraph AttentionRecommendation SystemsA Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
Traditional recommender systems (RS) typically use user-item rating histories as their main data source. However, deep generative models now have the capability to model and sample from complex data distributions, includ…
Collaborative FilteringRecommendation SystemsRetrievalSurveyKGUF: Simple Knowledge-aware Graph-based Recommender with User-based Semantic Features Filtering
The recent integration of Graph Neural Networks (GNNs) into recommendation has led to a novel family of Collaborative Filtering (CF) approaches, namely Graph Collaborative Filtering (GCF). Following the same GNNs wave, r…
Collaborative FilteringKnowledge GraphsRecommendation SystemsEnhanced Bayesian Personalized Ranking for Robust Hard Negative Sampling in Recommender Systems
In implicit collaborative filtering, hard negative mining techniques are developed to accelerate and enhance the recommendation model learning. However, the inadvertent selection of false negatives remains a major concer…
Collaborative FilteringRecommendation SystemsLightweight Embeddings for Graph Collaborative Filtering
Graph neural networks (GNNs) are currently one of the most performant collaborative filtering methods. Meanwhile, owing to the use of an embedding table to represent each user/item as a distinct vector, GNN-based recomme…
Collaborative FilteringSemantic SimilaritySemantic Textual SimilarityContent-Based Collaborative Generation for Recommender Systems
Generative models have emerged as a promising utility to enhance recommender systems. It is essential to model both item content and user-item collaborative interactions in a unified generative framework for better recom…
Collaborative FilteringLanguage ModellingLarge Language ModelRecommendation Systems+1Sequential Recommendation with Latent Relations based on Large Language Model
Sequential recommender systems predict items that may interest users by modeling their preferences based on historical interactions. Traditional sequential recommendation methods rely on capturing implicit collaborative …
Collaborative FilteringKnowledge GraphsLanguage ModelingLanguage Modelling+5How Does Message Passing Improve Collaborative Filtering?
Collaborative filtering (CF) has exhibited prominent results for recommender systems and been broadly utilized for real-world applications. A branch of research enhances CF methods by message passing used in graph neural…
Collaborative FilteringRecommendation SystemsTAGAll-in-One: Heterogeneous Interaction Modeling for Cold-Start Rating Prediction
Cold-start rating prediction is a fundamental problem in recommender systems that has been extensively studied. Many methods have been proposed that exploit explicit relations among existing data, such as collaborative f…
AllCollaborative FilteringRecommendation SystemsLarge Language Models Enhanced Collaborative Filtering
Recent advancements in Large Language Models (LLMs) have attracted considerable interest among researchers to leverage these models to enhance Recommender Systems (RSs). Existing work predominantly utilizes LLMs to gener…
Collaborative FilteringIn-Context LearningRecommendation SystemsWorld KnowledgeAFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture collaborative signals within intricate user-i…
Collaborative FilteringRecommendation SystemsKnowledge-aware Dual-side Attribute-enhanced Recommendation
\textit{Knowledge-aware} recommendation methods (KGR) based on \textit{graph neural networks} (GNNs) and \textit{contrastive learning} (CL) have achieved promising performance. However, they fall short in modeling fine-g…
AttributeCollaborative FilteringContrastive LearningKnowledge-Aware Recommendation+1Bilateral Unsymmetrical Graph Contrastive Learning for Recommendation
Recent methods utilize graph contrastive Learning within graph-structured user-item interaction data for collaborative filtering and have demonstrated their efficacy in recommendation tasks. However, they ignore that the…
Collaborative FilteringContrastive LearningRelationKnowledge-Enhanced Recommendation with User-Centric Subgraph Network
Recommendation systems, as widely implemented nowadays on various platforms, recommend relevant items to users based on their preferences. The classical methods which rely on user-item interaction matrices has limitation…
Collaborative FilteringGraph Neural NetworkRecommendation SystemsUse of recommendation models to provide support to dyslexic students
Dyslexia is the most widespread specific learning disorder and significantly impair different cognitive domains. This, in turn, negatively affects dyslexic students during their learning path. Therefore, specific support…
Collaborative FilteringAccelerating Matrix Factorization by Dynamic Pruning for Fast Recommendation
Matrix factorization (MF) is a widely used collaborative filtering (CF) algorithm for recommendation systems (RSs), due to its high prediction accuracy, great flexibility and high efficiency in big data processing. Howev…
Collaborative FilteringRecommendation SystemsSelf-supervised Contrastive Learning for Implicit Collaborative Filtering
Contrastive learning-based recommendation algorithms have significantly advanced the field of self-supervised recommendation, particularly with BPR as a representative ranking prediction task that dominates implicit coll…
Collaborative FilteringContrastive LearningRecommendation SystemsEmerging Synergies Between Large Language Models and Machine Learning in Ecommerce Recommendations
With the boom of e-commerce and web applications, recommender systems have become an important part of our daily lives, providing personalized recommendations based on the user's preferences. Although deep neural network…
Collaborative FilteringRecommendation Systems