Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Kernel-CF: Collaborative filtering done right with social network analysis and kernel smoothing
Collaborative filtering is the simplest but oldest machine learning algorithm in the field of recommender systems. In spite of its long history, it remains a discussion topic in research venues. Usually people use users/…
Collaborative FilteringRecommendation SystemsCreating Synthetic Datasets for Collaborative Filtering Recommender Systems using Generative Adversarial Networks
Research and education in machine learning needs diverse, representative, and open datasets that contain sufficient samples to handle the necessary training, validation, and testing tasks. Currently, the Recommender Syst…
Collaborative FilteringGenerative Adversarial NetworkRecommendation SystemsPredicting Privacy Preferences for Smart Devices as Norms
Smart devices, such as smart speakers, are becoming ubiquitous, and users expect these devices to act in accordance with their preferences. In particular, since these devices gather and manage personal data, users expect…
Collaborative FilteringManaging multi-facet bias in collaborative filtering recommender systems
Due to the extensive growth of information available online, recommender systems play a more significant role in serving people's interests. Traditional recommender systems mostly use an accuracy-focused approach to prod…
Collaborative FilteringRecommendation SystemsMulti-Behavior Graph Neural Networks for Recommender System
Recommender systems have been demonstrated to be effective to meet user's personalized interests for many online services (e.g., E-commerce and online advertising platforms). Recent years have witnessed the emerging succ…
Collaborative FilteringGraph Neural NetworkRecommendation SystemsTAGImproving Recommendation Fairness via Data Augmentation
Collaborative filtering based recommendation learns users' preferences from all users' historical behavior data, and has been popular to facilitate decision making. R Recently, the fairness issue of recommendation has be…
Collaborative FilteringData AugmentationDecision MakingFairness+1Denoising and Prompt-Tuning for Multi-Behavior Recommendation
In practical recommendation scenarios, users often interact with items under multi-typed behaviors (e.g., click, add-to-cart, and purchase). Traditional collaborative filtering techniques typically assume that users only…
Collaborative FilteringDenoisingAdversarial Online Collaborative Filtering
We investigate the problem of online collaborative filtering under no-repetition constraints, whereby users need to be served content in an online fashion and a given user cannot be recommended the same content item more…
Collaborative FilteringInvariant Collaborative Filtering to Popularity Distribution Shift
Collaborative Filtering (CF) models, despite their great success, suffer from severe performance drops due to popularity distribution shifts, where these changes are ubiquitous and inevitable in real-world scenarios. Unf…
Collaborative FilteringRepresentation LearningSemi-decentralized Federated Ego Graph Learning for Recommendation
Collaborative filtering (CF) based recommender systems are typically trained based on personal interaction data (e.g., clicks and purchases) that could be naturally represented as ego graphs. However, most existing recom…
Collaborative FilteringGraph LearningGraph Neural NetworkPrivacy Preserving+1Lorentz Equivariant Model for Knowledge-Enhanced Hyperbolic Collaborative Filtering
Introducing prior auxiliary information from the knowledge graph (KG) to assist the user-item graph can improve the comprehensive performance of the recommender system. Many recent studies show that the ensemble properti…
AttributeCollaborative FilteringRecommendation SystemsTowards Lightweight Cross-domain Sequential Recommendation via External Attention-enhanced Graph Convolution Network
Cross-domain Sequential Recommendation (CSR) is an emerging yet challenging task that depicts the evolution of behavior patterns for overlapped users by modeling their interactions from multiple domains. Existing studies…
Collaborative FilteringSequential RecommendationFederated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction
In this study, we propose a novel SeqMF model to solve the problem of predicting the next app launch during mobile device usage. Although this problem can be represented as a classical collaborative filtering problem, it…
Collaborative FilteringFederated LearningPrivacy PreservingContrastive Collaborative Filtering for Cold-Start Item Recommendation
The cold-start problem is a long-standing challenge in recommender systems. As a promising solution, content-based generative models usually project a cold-start item's content onto a warm-start item embedding to capture…
Collaborative FilteringContrastive LearningRecommendation SystemsPersonalized Graph Signal Processing for Collaborative Filtering
The collaborative filtering (CF) problem with only user-item interaction information can be solved by graph signal processing (GSP), which uses low-pass filters to smooth the observed interaction signals on the similarit…
Collaborative FilteringKNNs of Semantic Encodings for Rating Prediction
This paper explores a novel application of textual semantic similarity to user-preference representation for rating prediction. The approach represents a user's preferences as a graph of textual snippets from review text…
Collaborative FilteringPredictionSemantic SimilaritySemantic Textual SimilarityJob recommendations: benchmarking of collaborative filtering methods for classifieds
Classifieds provide many challenges for recommendation methods, due to the limited information regarding users and items. In this paper, we explore recommendation methods for classifieds using the example of OLX Jobs. Th…
BenchmarkingCollaborative FilteringMitigating Human and Computer Opinion Fraud via Contrastive Learning
We introduce the novel approach towards fake text reviews detection in collaborative filtering recommender systems. The existing algorithms concentrate on detecting the fake reviews, generated by language models and igno…
Collaborative FilteringContrastive LearningRecommendation SystemsMultidimensional Item Response Theory in the Style of Collaborative Filtering
This paper presents a machine learning approach to multidimensional item response theory (MIRT), a class of latent factor models that can be used to model and predict student performance from observed assessment data. In…
Collaborative FilteringRecommendation SystemsOntology-based Context Aware Recommender System Application for Tourism
In this work a novel recommender system (RS) for Tourism is presented. The RS is context aware as is now the rule in the state-of-the-art for recommender systems and works on top of a tourism ontology which is used to gr…
Collaborative FilteringRecommendation Systems