Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Robust Collaborative Filtering to Popularity Distribution Shift
In leading collaborative filtering (CF) models, representations of users and items are prone to learn popularity bias in the training data as shortcuts. The popularity shortcut tricks are good for in-distribution (ID) pe…
Collaborative FilteringWhen Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendations
We study the fairness of dimensionality reduction methods for recommendations. We focus on the fundamental method of principal component analysis (PCA), which identifies latent components and produces a low-rank approxim…
Collaborative FilteringDimensionality ReductionFairnessAutomatic Music Playlist Generation via Simulation-based Reinforcement Learning
Personalization of playlists is a common feature in music streaming services, but conventional techniques, such as collaborative filtering, rely on explicit assumptions regarding content quality to learn how to make reco…
Collaborative Filteringreinforcement-learningReinforcement LearningAgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems
Recently, there has been an emergence of employing LLM-powered agents as believable human proxies, based on their remarkable decision-making capability. However, existing studies mainly focus on simulating human dialogue…
Collaborative FilteringDecision MakingLanguage ModelingLanguage Modelling+1Collaborative Semantic Alignment in Recommendation Systems
Traditional recommender systems primarily leverage identity-based (ID) representations for users and items, while the advent of pre-trained language models (PLMs) has introduced rich semantic modeling of item description…
Collaborative FilteringLanguage ModellingRecommendation SystemsAddressing the cold start problem in privacy preserving content-based recommender systems using hypercube graphs
The initial interaction of a user with a recommender system is problematic because, in such a so-called cold start situation, the recommender system has very little information about the user, if any. Moreover, in collab…
Collaborative FilteringPrivacy PreservingRecommendation SystemsMetrics for popularity bias in dynamic recommender systems
Albeit the widespread application of recommender systems (RecSys) in our daily lives, rather limited research has been done on quantifying unfairness and biases present in such systems. Prior work largely focuses on dete…
Collaborative FilteringRecommendation SystemsTen Challenges in Industrial Recommender Systems
Huawei's vision and mission is to build a fully connected intelligent world. Since 2013, Huawei Noah's Ark Lab has helped many products build recommender systems and search engines for getting the right information to th…
Collaborative FilteringRecommendation SystemsBeyond-Accuracy: A Review on Diversity, Serendipity and Fairness in Recommender Systems Based on Graph Neural Networks
By providing personalized suggestions to users, recommender systems have become essential to numerous online platforms. Collaborative filtering, particularly graph-based approaches using Graph Neural Networks (GNNs), hav…
Collaborative FilteringDiversityFairnessgraph construction+1Matrix Factorization in Tropical and Mixed Tropical-Linear Algebras
Matrix Factorization (MF) has found numerous applications in Machine Learning and Data Mining, including collaborative filtering recommendation systems, dimensionality reduction, data visualization, and community detecti…
Collaborative FilteringCommunity DetectionData VisualizationDimensionality Reduction+1Enhancing Graph Collaborative Filtering via Uniformly Co-Clustered Intent Modeling
Graph-based collaborative filtering has emerged as a powerful paradigm for delivering personalized recommendations. Despite their demonstrated effectiveness, these methods often neglect the underlying intents of users, w…
Collaborative FilteringMitigating the Popularity Bias of Graph Collaborative Filtering: A Dimensional Collapse Perspective
Graph-based Collaborative Filtering (GCF) is widely used in personalized recommendation systems. However, GCF suffers from a fundamental problem where features tend to occupy the embedding space inefficiently (by spannin…
Collaborative FilteringRecommendation SystemsMitigating the Popularity Bias of Graph Collaborative Filtering: A Dimensional Collapse Perspective
Graph-based Collaborative Filtering (GCF) is widely used in personalized recommendation systems. However, GCF suffers from a fundamental problem where features tend to occupy the embedding space inefficiently (by spannin…
Collaborative FilteringDiversityRecommendation SystemsPrivate Matrix Factorization with Public Item Features
We consider the problem of training private recommendation models with access to public item features. Training with Differential Privacy (DP) offers strong privacy guarantees, at the expense of loss in recommendation qu…
Collaborative FilteringScalable Approximate NonSymmetric Autoencoder for Collaborative Filtering
In the field of recommender systems, shallow autoencoders have recently gained significant attention. One of the most highly acclaimed shallow autoencoders is EASE, favored for its competitive recommendation accuracy and…
Collaborative FilteringRecommendation SystemsInteractive Graph Convolutional Filtering
Interactive Recommender Systems (IRS) have been increasingly used in various domains, including personalized article recommendation, social media, and online advertising. However, IRS faces significant challenges in prov…
Collaborative FilteringMeta-LearningRecommendation SystemsVariational InferencePre-trained Neural Recommenders: A Transferable Zero-Shot Framework for Recommendation Systems
Modern neural collaborative filtering techniques are critical to the success of e-commerce, social media, and content-sharing platforms. However, despite technical advances -- for every new application domain, we need to…
Collaborative FilteringRecommendation SystemsLearning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization
Category information plays a crucial role in enhancing the quality and personalization of recommender systems. Nevertheless, the availability of item category information is not consistently present, particularly in the …
Click-Through Rate PredictionCollaborative FilteringEntity EmbeddingsQuantization+1Knowledge-grounded Natural Language Recommendation Explanation
Explanations accompanied by a recommendation can assist users in understanding the decision made by recommendation systems, which in turn increases a user's confidence and trust in the system. Recently, research has focu…
Collaborative FilteringExplainable RecommendationRecommendation SystemsMultimodal Recommender Systems in the Prediction of Disease Comorbidity
While deep-learning based recommender systems utilizing collaborative filtering have been commonly used for recommendation in other domains, their application in the medical domain have been limited. In addition to model…
Collaborative FilteringDeep LearningRecommendation Systems