Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Joint Variational Autoencoders for Recommendation with Implicit Feedback
Variational Autoencoders (VAEs) have recently shown promising performance in collaborative filtering with implicit feedback. These existing recommendation models learn user representations to reconstruct or predict user …
Collaborative FilteringPerformance of Hyperbolic Geometry Models on Top-N Recommendation Tasks
We introduce a simple autoencoder based on hyperbolic geometry for solving standard collaborative filtering problem. In contrast to many modern deep learning techniques, we build our solution using only a single hidden l…
Collaborative FilteringA Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Completion
Matrix learning is at the core of many machine learning problems. A number of real-world applications such as collaborative filtering and text mining can be formulated as a low-rank matrix completion problem, which recov…
Collaborative FilteringLow-Rank Matrix CompletionMatrix CompletionNeural collaborative filtering for unsupervised mitral valve segmentation in echocardiography
The segmentation of the mitral valve annulus and leaflets specifies a crucial first step to establish a machine learning pipeline that can support physicians in performing multiple tasks, e.g.\ diagnosis of mitral valve …
Collaborative FilteringSegmentationImproving information retrieval from electronic health records using dynamic and multi-collaborative filtering
Due to the rapid growth of information available about individual patients, most physicians suffer from information overload when they review patient information in health information technology systems. In this manuscri…
Collaborative FilteringInformation RetrievalRecommendation SystemsRetrievalOnline Graph Completion: Multivariate Signal Recovery in Computer Vision
The adoption of "human-in-the-loop" paradigms in computer vision and machine learning is leading to various applications where the actual data acquisition (e.g., human supervision) and the underlying inference algorithms…
Active LearningCollaborative FilteringExperimental DesignScientific Paper Recommendation: A Survey
Globally, recommendation services have become important due to the fact that they support e-commerce applications and different research communities. Recommender systems have a large number of applications in many fields…
ArticlesCollaborative FilteringRecommendation SystemsSurveyNoise Contrastive Estimation for Autoencoding-based One-Class Collaborative Filtering
One-class collaborative filtering (OC-CF) is a common class of recommendation problem where only the positive class is explicitly observed (e.g., purchases, clicks). Autoencoder based recommenders such as AutoRec and var…
Collaborative FilteringCOMET: Convolutional Dimension Interaction for Collaborative Filtering
Representation learning-based recommendation models play a dominant role among recommendation techniques. However, most of the existing methods assume both historical interactions and embedding dimensions are independent…
Collaborative FilteringRepresentation LearningDo recommender systems function in the health domain: a system review
Recommender systems have fulfilled an important role in everyday life. Recommendations such as news by Google, videos by Netflix, goods by e-commerce providers, etc. have heavily changed everyones lifestyle. Health domai…
Collaborative FilteringDecision MakingRecommendation SystemsHybrid Collaborative Filtering Models for Clinical Search Recommendation
With increasing and extensive use of electronic health records, clinicians are often under time pressure when they need to retrieve important information efficiently among large amounts of patients' health records in cli…
Collaborative FilteringRecommendation SystemsExplainable Recommendation via Interpretable Feature Mapping and Evaluation of Explainability
Latent factor collaborative filtering (CF) has been a widely used technique for recommender system by learning the semantic representations of users and items. Recently, explainable recommendation has attracted much atte…
Collaborative FilteringExplainable RecommendationRecommendation SystemsTowards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach
Recommendation models can effectively estimate underlying user interests and predict one's future behaviors by factorizing an observed user-item rating matrix into products of two sets of latent factors. However, the use…
Collaborative FilteringMatrix CompletionRecommendation SystemsRepresentation LearningRGCF: Refined Graph Convolution Collaborative Filtering with concise and expressive embedding
Graph Convolution Network (GCN) has attracted significant attention and become the most popular method for learning graph representations. In recent years, many efforts have been focused on integrating GCN into the recom…
Collaborative FilteringNeural Interactive Collaborative Filtering
In this paper, we study collaborative filtering in an interactive setting, in which the recommender agents iterate between making recommendations and updating the user profile based on the interactive feedback. The most …
Collaborative FilteringMeta-LearningQ-LearningRecommendation SystemsDisentangled Graph Collaborative Filtering
Learning informative representations of users and items from the interaction data is of crucial importance to collaborative filtering (CF). Present embedding functions exploit user-item relationships to enrich the repres…
Collaborative FilteringDisentanglementPrivacy Threats Against Federated Matrix Factorization
Matrix Factorization has been very successful in practical recommendation applications and e-commerce. Due to data shortage and stringent regulations, it can be hard to collect sufficient data to build performant recomme…
Collaborative FilteringFederated LearningPrivacy PreservingRecommendation SystemsText-based Emotion Aware Recommender
We apply the concept of users' emotion vectors (UVECs) and movies' emotion vectors (MVECs) as building components of Emotion Aware Recommender System. We built a comparative platform that consists of five recommenders ba…
Collaborative FilteringRecommendation SystemsItem-based Collaborative Filtering with BERT
In e-commerce, recommender systems have become an indispensable part of helping users explore the available inventory. In this work, we present a novel approach for item-based collaborative filtering, by leveraging BERT …
Collaborative FilteringRecommendation SystemsSemi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation
Data sparsity is an inherent challenge in the recommender systems, where most of the data is collected from the implicit feedbacks of users. This causes two difficulties in designing effective algorithms: first, the majo…
Collaborative FilteringDomain AdaptationRecommendation Systems