Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Popularity Debiasing from Exposure to Interaction in Collaborative Filtering
Recommender systems often suffer from popularity bias, where popular items are overly recommended while sacrificing unpopular items. Existing researches generally focus on ensuring the number of recommendations exposure …
Collaborative FilteringRecommendation SystemsWSFE: Wasserstein Sub-graph Feature Encoder for Effective User Segmentation in Collaborative Filtering
Maximizing the user-item engagement based on vectorized embeddings is a standard procedure of recent recommender models. Despite the superior performance for item recommendations, these methods however implicitly deprior…
Collaborative FilteringDisentangled Contrastive Collaborative Filtering
Recent studies show that graph neural networks (GNNs) are prevalent to model high-order relationships for collaborative filtering (CF). Towards this research line, graph contrastive learning (GCL) has exhibited powerful …
Collaborative FilteringContrastive LearningDisentanglementTowards Explainable Collaborative Filtering with Taste Clusters Learning
Collaborative Filtering (CF) is a widely used and effective technique for recommender systems. In recent decades, there have been significant advancements in latent embedding-based CF methods for improved accuracy, such …
Collaborative FilteringDecision MakingInformativenessRecommendation Systems+1Triple Structural Information Modelling for Accurate, Explainable and Interactive Recommendation
In dynamic interaction graphs, user-item interactions usually follow heterogeneous patterns, represented by different structural information, such as user-item co-occurrence, sequential information of user interactions a…
Collaborative FilteringInteractive RecommendationLeveraging Deep Learning Techniques on Collaborative Filtering Recommender Systems
With the exponentially increasing volume of online data, searching and finding required information have become an extensive and time-consuming task. Recommender Systems as a subclass of information retrieval and decisio…
Collaborative FilteringDeep LearningInformation RetrievalRecommendation Systems+1Collaborative Residual Metric Learning
In collaborative filtering, distance metric learning has been applied to matrix factorization techniques with promising results. However, matrix factorization lacks the ability of capturing collaborative information, whi…
Collaborative FilteringMetric LearningRecommendation SystemsHEAT: A Highly Efficient and Affordable Training System for Collaborative Filtering Based Recommendation on CPUs
Collaborative filtering (CF) has been proven to be one of the most effective techniques for recommendation. Among all CF approaches, SimpleX is the state-of-the-art method that adopts a novel loss function and a proper n…
Collaborative FilteringCPUGPUThe Future of ChatGPT-enabled Labor Market: A Preliminary Study in China
As a phenomenal large language model, ChatGPT has achieved unparalleled success in various real-world tasks and increasingly plays an important role in our daily lives and work. However, extensive concerns are also raise…
Collaborative FilteringLanguage ModellingLarge Language ModelSheaf4Rec: Sheaf Neural Networks for Graph-based Recommender Systems
Recent advancements in Graph Neural Networks (GNN) have facilitated their widespread adoption in various applications, including recommendation systems. GNNs have proven to be effective in addressing the challenges posed…
Collaborative FilteringLink PredictionRecommendation SystemsHGCH: A Hyperbolic Graph Convolution Network Model for Heterogeneous Collaborative Graph Recommendation
User-item interaction data in collaborative filtering and graph modeling tasks often exhibit power-law characteristics, which suggest the suitability of hyperbolic space modeling. Hyperbolic Graph Convolution Neural Netw…
Collaborative FilteringGraph Collaborative Signals Denoising and Augmentation for Recommendation
Graph collaborative filtering (GCF) is a popular technique for capturing high-order collaborative signals in recommendation systems. However, GCF's bipartite adjacency matrix, which defines the neighbors being aggregated…
Collaborative FilteringDenoisingRecommendation SystemsSequence-aware item recommendations for multiply repeated user-item interactions
Recommender systems are one of the most successful applications of machine learning and data science. They are successful in a wide variety of application domains, including e-commerce, media streaming content, email mar…
Collaborative FilteringMarketingMatrix CompletionRecommendation SystemsItem Graph Convolution Collaborative Filtering for Inductive Recommendations
Graph Convolutional Networks (GCN) have been recently employed as core component in the construction of recommender system algorithms, interpreting user-item interactions as the edges of a bipartite graph. However, in th…
Collaborative FilteringRecommendation SystemsRecommendation Systems in Libraries: an Application with Heterogeneous Data Sources
The Reading&Machine project exploits the support of digitalization to increase the attractiveness of libraries and improve the users' experience. The project implements an application that helps the users in their decisi…
Collaborative FilteringDecision MakingRecommendation SystemsExplaining the Performance of Collaborative Filtering Methods With Optimal Data Characteristics
The performance of a Collaborative Filtering (CF) method is based on the properties of a User-Item Rating Matrix (URM). And the properties or Rating Data Characteristics (RDC) of a URM are constantly changing. Recent stu…
Collaborative FilteringFairness-aware Differentially Private Collaborative Filtering
Recently, there has been an increasing adoption of differential privacy guided algorithms for privacy-preserving machine learning tasks. However, the use of such algorithms comes with trade-offs in terms of algorithmic f…
Collaborative FilteringFairnessPrivacy PreservingGraph-less Collaborative Filtering
Graph neural networks (GNNs) have shown the power in representation learning over graph-structured user-item interaction data for collaborative filtering (CF) task. However, with their inherently recursive message propag…
Collaborative FilteringContrastive LearningKnowledge DistillationRepresentation LearningAutomated Self-Supervised Learning for Recommendation
Graph neural networks (GNNs) have emerged as the state-of-the-art paradigm for collaborative filtering (CF). To improve the representation quality over limited labeled data, contrastive learning has attracted attention i…
Collaborative FilteringContrastive LearningData AugmentationSelf-Supervised LearningNeural Group Recommendation Based on a Probabilistic Semantic Aggregation
Recommendation to groups of users is a challenging subfield of recommendation systems. Its key concept is how and where to make the aggregation of each set of user information into an individual entity, such as a ranked …
Collaborative FilteringRecommendation Systems