Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Deep Learning feature selection to unhide demographic recommender systems factors
Extracting demographic features from hidden factors is an innovative concept that provides multiple and relevant applications. The matrix factorization model generates factors which do not incorporate semantic knowledge.…
Collaborative FilteringDeep LearningFairnessfeature selection+2Hybrid Attentional Memory Network for Computational drug repositioning
Drug repositioning is designed to discover new uses of known drugs, which is an important and efficient method of drug discovery. Researchers only use one certain type of Collaborative Filtering (CF) models for drug repo…
Collaborative FilteringDrug DiscoverySupervised Learning of Sparsity-Promoting Regularizers for Denoising
We present a method for supervised learning of sparsity-promoting regularizers for image denoising. Sparsity-promoting regularization is a key ingredient in solving modern image reconstruction problems; however, the oper…
Collaborative FilteringDenoisingDictionary LearningImage Denoising+1DeepFair: Deep Learning for Improving Fairness in Recommender Systems
The lack of bias management in Recommender Systems leads to minority groups receiving unfair recommendations. Moreover, the trade-off between equity and precision makes it difficult to obtain recommendations that meet bo…
Collaborative FilteringDeep LearningFairnessManagement+1Exploration-Exploitation Motivated Variational Auto-Encoder for Recommender Systems
Recent years have witnessed rapid developments on collaborative filtering techniques for improving the performance of recommender systems due to the growing need of companies to help users discover new and relevant items…
Collaborative FilteringRecommendation SystemsProviding reliability in Recommender Systems through Bernoulli Matrix Factorization
Beyond accuracy, quality measures are gaining importance in modern recommender systems, with reliability being one of the most important indicators in the context of collaborative filtering. This paper proposes Bernoulli…
Collaborative FilteringGeneral ClassificationRecommendation SystemsFairness-Aware Explainable Recommendation over Knowledge Graphs
There has been growing attention on fairness considerations recently, especially in the context of intelligent decision making systems. Explainable recommendation systems, in particular, may suffer from both explanation …
Collaborative FilteringDecision MakingExplainable RecommendationFairness+3Content-aware Neural Hashing for Cold-start Recommendation
Content-aware recommendation approaches are essential for providing meaningful recommendations for \textit{new} (i.e., \textit{cold-start}) items in a recommender system. We present a content-aware neural hashing-based c…
Collaborative FilteringRecommendation SystemsWould you Like to Talk about Sports Now? Towards Contextual Topic Suggestion for Open-Domain Conversational Agents
To hold a true conversation, an intelligent agent should be able to occasionally take initiative and recommend the next natural conversation topic. This is a challenging task. A topic suggested by the agent should be rel…
Collaborative FilteringSeamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users
Static recommendation methods like collaborative filtering suffer from the inherent limitation of performing real-time personalization for cold-start users. Online recommendation, e.g., multi-armed bandit approach, addre…
Collaborative FilteringConversational RecommendationThompson SamplingTransfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation
The rapid proliferation of new users and items on the social web has aggravated the gray-sheep user/long-tail item challenge in recommender systems. Historically, cross-domain co-clustering methods have successfully leve…
ClusteringCollaborative FilteringRecommendation SystemsTransfer LearningA survey on Adversarial Recommender Systems: from Attack/Defense strategies to Generative Adversarial Networks
Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization (MF) and deep CF methods, are widely used in modern recommender systems (RS) due to their excellent performance and recommend…
ArticlesBIG-bench Machine LearningCollaborative FilteringRecommendation Systems+1Try This Instead: Personalized and Interpretable Substitute Recommendation
As a fundamental yet significant process in personalized recommendation, candidate generation and suggestion effectively help users spot the most suitable items for them. Consequently, identifying substitutable items tha…
AttributeCollaborative FilteringSentiment AnalysisNeural Collaborative Filtering vs. Matrix Factorization Revisited
Embedding based models have been the state of the art in collaborative filtering for over a decade. Traditionally, the dot product or higher order equivalents have been used to combine two or more embeddings, e.g., most …
Collaborative FilteringLink PredictionRetrievalA Hybrid Approach to Enhance Pure Collaborative Filtering based on Content Feature Relationship
Recommendation systems get expanding significance because of their applications in both the scholarly community and industry. With the development of additional data sources and methods of extracting new information othe…
Collaborative FilteringRecommendation SystemsNeural Collaborative Reasoning
Existing Collaborative Filtering (CF) methods are mostly designed based on the idea of matching, i.e., by learning user and item embeddings from data using shallow or deep models, they try to capture the associative rele…
Collaborative FilteringDecision MakingLogical ReasoningRecommendation Systems+1Utilizing FastText for Venue Recommendation
Venue recommendation systems model the past interactions (i.e., check-ins) of the users and recommend venues. Traditional recommendation systems employ collaborative filtering, content-based filtering or matrix factoriza…
Collaborative FilteringRecommendation SystemsVariational Autoencoders for Highly Multivariate Spatial Point Processes Intensities
Multivariate spatial point process models can describe heterotopic data over space. However, highly multivariate intensities are computationally challenging due to the curse of dimensionality. To bridge this gap, we intr…
Collaborative FilteringPoint ProcessesRecommendation SystemsRaCT: Toward Amortized Ranking-Critical Training For Collaborative Filtering
We investigate new methods for training collaborative filtering models based on actor-critic reinforcement learning, to more directly maximize ranking-based objective functions. Specifically, we train a critic network to…
Collaborative FilteringLearning-To-Rankreinforcement-learningReinforcement Learning (RL)A Robust Hierarchical Graph Convolutional Network Model for Collaborative Filtering
Graph Convolutional Network (GCN) has achieved great success and has been applied in various fields including recommender systems. However, GCN still suffers from many issues such as training difficulties, over-smoothing…
Collaborative FilteringRecommendation Systems