Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
PolyCF: Towards the Optimal Spectral Graph Filters for Collaborative Filtering
Collaborative Filtering (CF) is a pivotal research area in recommender systems that capitalizes on collaborative similarities between users and items to provide personalized recommendations. With the remarkable achieveme…
Collaborative FilteringRecommendation SystemsWhat Are We Optimizing For? A Human-centric Evaluation of Deep Learning-based Movie Recommenders
In the past decade, deep learning (DL) models have gained prominence for their exceptional accuracy on benchmark datasets in recommender systems (RecSys). However, their evaluation has primarily relied on offline metrics…
AttributeCollaborative FilteringDiversityRecommendation SystemsUnderstanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recency
This paper explores the biases in ChatGPT-based recommender systems, focusing on provider fairness (item-side fairness). Through extensive experiments and over a thousand API calls, we investigate the impact of prompt de…
Collaborative FilteringFairnessIn-Context LearningRecommendation Systems+1Adaptive Hardness Negative Sampling for Collaborative Filtering
Negative sampling is essential for implicit collaborative filtering to provide proper negative training signals so as to achieve desirable performance. We experimentally unveil a common limitation of all existing negativ…
Collaborative FilteringDualVAE: Dual Disentangled Variational AutoEncoder for Recommendation
Learning precise representations of users and items to fit observed interaction data is the fundamental task of collaborative filtering. Existing studies usually infer entangled representations to fit such interaction da…
Collaborative FilteringDisentanglementRepresentation LearningVariational InferencePrivacy-Preserving Sequential Recommendation with Collaborative Confusion
Sequential recommendation has attracted a lot of attention from both academia and industry, however the privacy risks associated to gathering and transferring users' personal interaction data are often underestimated or …
Collaborative FilteringFederated LearningPrivacy PreservingSequential RecommendationThe Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias
Collaborative filtering-based recommender systems leverage vast amounts of behavioral user data, which poses severe privacy risks. Thus, often, random noise is added to the data to ensure Differential Privacy (DP). Howev…
Collaborative FilteringRecommendation SystemsEXPLORE -- Explainable Song Recommendation
This study explores the development of an explainable music recommendation system with enhanced user control. Leveraging a hybrid of collaborative filtering and content-based filtering, we address the challenges of opaqu…
Collaborative FilteringMusic RecommendationTowards Mitigating Dimensional Collapse of Representations in Collaborative Filtering
Contrastive Learning (CL) has shown promising performance in collaborative filtering. The key idea is to generate augmentation-invariant embeddings by maximizing the Mutual Information between different augmented views o…
Collaborative FilteringContrastive LearningData AugmentationA Comprehensive Survey of Evaluation Techniques for Recommendation Systems
The effectiveness of recommendation systems is pivotal to user engagement and satisfaction in online platforms. As these recommendation systems increasingly influence user choices, their evaluation transcends mere techni…
Collaborative FilteringRecommendation SystemsSurveyTemporally and Distributionally Robust Optimization for Cold-Start Recommendation
Collaborative Filtering (CF) recommender models highly depend on user-item interactions to learn CF representations, thus falling short of recommending cold-start items. To address this issue, prior studies mainly introd…
Collaborative FilteringAT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction
Click-through rate (CTR) prediction is a vital task in industrial recommendation systems. Most existing methods focus on the network architecture design of the CTR model for better accuracy and suffer from the data spars…
Click-Through Rate PredictionCollaborative FilteringContrastive LearningRecommendation SystemsSoft Frequency Capping for Improved Ad Click Prediction in Yahoo Gemini Native
Yahoo's native advertising (also known as Gemini native) serves billions of ad impressions daily, reaching a yearly run-rate of many hundred of millions USD. Driving the Gemini native models that are used to predict both…
Collaborative FilteringUnbiased Filtering Of Accidental Clicks in Verizon Media Native Advertising
Verizon Media (VZM) native advertising is one of VZM largest and fastest growing businesses, reaching a run-rate of several hundred million USDs in the past year. Driving the VZM native models that are used to predict ev…
Collaborative FilteringSynergistic Signals: Exploiting Co-Engagement and Semantic Links via Graph Neural Networks
Given a set of candidate entities (e.g. movie titles), the ability to identify similar entities is a core capability of many recommender systems. Most often this is achieved by collaborative filtering approaches, i.e. if…
Collaborative FilteringGraph Neural NetworkRecommendation SystemsRepresentation LearningAdaptive spectral graph wavelets for collaborative filtering
Collaborative filtering is a popular approach in recommender systems, whose objective is to provide personalized item suggestions to potential users based on their purchase or browsing history. However, personalized reco…
Collaborative FilteringRecommendation SystemsDynamic Collaborative Filtering for Matrix- and Tensor-based Recommender Systems
In production applications of recommender systems, a continuous data flow is employed to update models in real-time. Many recommender models often require complete retraining to adapt to new data. In this work, we introd…
Collaborative FilteringRecommendation SystemsPreference and Concurrence Aware Bayesian Graph Neural Networks for Recommender Systems
Graph-based collaborative filtering methods have prevailing performance for recommender systems since they can capture high-order information between users and items, in which the graphs are constructed from the observed…
Collaborative FilteringGraph Neural NetworkRecommendation SystemsHierarchical Matrix Factorization for Interpretable Collaborative Filtering
Matrix factorization (MF) is a simple collaborative filtering technique that achieves superior recommendation accuracy by decomposing the user-item interaction matrix into user and item latent matrices. Because the model…
ClusteringCollaborative FilteringGraph Neural Ordinary Differential Equations-based method for Collaborative Filtering
Graph Convolution Networks (GCNs) are widely considered state-of-the-art for collaborative filtering. Although several GCN-based methods have been proposed and achieved state-of-the-art performance in various tasks, they…
Collaborative Filtering