Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Collaborative filtering via heterogeneous neural networks
After being proved extremely useful in many applications, the network embedding has played a critical role in the network analysis. Most of recent works usually model the network by minimizing the joint probability that …
Collaborative FilteringInformativenessLink PredictionNetwork EmbeddingNeural Collaborative Filtering Bandits via Meta Learning
Contextual multi-armed bandits provide powerful tools to solve the exploitation-exploration dilemma in decision making, with direct applications in the personalized recommendation. In fact, collaborative effects among us…
Collaborative FilteringDecision MakingMeta-LearningMulti-Armed BanditsCoordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms
Motivated by online recommendation systems, we propose the problem of finding the optimal policy in multitask contextual bandits when a small fraction $\alpha < 1/2$ of tasks (users) are arbitrary and adversarial. The re…
Collaborative FilteringMulti-Armed BanditsRecommendation SystemsConsistent Collaborative Filtering via Tensor Decomposition
Collaborative filtering is the de facto standard for analyzing users' activities and building recommendation systems for items. In this work we develop Sliced Anti-symmetric Decomposition (SAD), a new model for collabora…
Collaborative FilteringRecommendation SystemsTensor DecompositionSparsity Regularization For Cold-Start Recommendation
Recently, Generative Adversarial Networks (GANs) have been applied to the problem of Cold-Start Recommendation, but the training performance of these models is hampered by the extreme sparsity in warm user purchase behav…
Collaborative FilteringExplainability in Music Recommender Systems
The most common way to listen to recorded music nowadays is via streaming platforms which provide access to tens of millions of tracks. To assist users in effectively browsing these large catalogs, the integration of Mus…
Collaborative FilteringExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)Music Recommendation+1Knowledge Graph Based Waveform Recommendation: A New Communication Waveform Design Paradigm
Traditionally, a communication waveform is designed by experts based on communication theory and their experiences on a case-by-case basis, which is usually laborious and time-consuming. In this paper, we investigate the…
Collaborative FilteringIntelligent CommunicationRepresentation LearningGAN-based Matrix Factorization for Recommender Systems
Proposed in 2014, Generative Adversarial Networks (GAN) initiated a fresh interest in generative modelling. They immediately achieved state-of-the-art in image synthesis, image-to-image translation, text-to-image generat…
Collaborative FilteringImage GenerationImage InpaintingImage-to-Image Translation+3Emergent Instabilities in Algorithmic Feedback Loops
Algorithms that aid human tasks, such as recommendation systems, are ubiquitous. They appear in everything from social media to streaming videos to online shopping. However, the feedback loop between people and algorithm…
Collaborative FilteringRecommendation SystemsMulti-Aspect co-Attentional Collaborative Filtering for Extreme Multi-label Text Classification
This work proposes a general and effective architecture for the extreme multi-label text classification (XMTC), and reformate the learning task to an interaction function between document and label. Recently, there are m…
Collaborative FilteringMulti Label Text ClassificationMulti-Label Text Classificationtext-classification+1On Sampling Collaborative Filtering Datasets
We study the practical consequences of dataset sampling strategies on the ranking performance of recommendation algorithms. Recommender systems are generally trained and evaluated on samples of larger datasets. Samples a…
Collaborative FilteringRecommendation SystemsCollaborative Reflection-Augmented Autoencoder Network for Recommender Systems
As the deep learning techniques have expanded to real-world recommendation tasks, many deep neural network based Collaborative Filtering (CF) models have been developed to project user-item interactions into latent featu…
Collaborative FilteringRecommendation SystemsSupervised Contrastive Learning for Recommendation
In this work, we aim to consider the application of contrastive learning in the scenario of the recommendation system adequately, making it more suitable for recommendation task. We propose a learning paradigm called sup…
Collaborative FilteringContrastive LearningSelf-Supervised LearningMulti-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling
Many previous studies aim to augment collaborative filtering with deep neural network techniques, so as to achieve better recommendation performance. However, most existing deep learning-based recommender systems are des…
Collaborative FilteringRecommendation SystemsRelationAn Evaluation Study of Generative Adversarial Networks for Collaborative Filtering
This work explores the reproducibility of CFGAN. CFGAN and its family of models (TagRec, MTPR, and CRGAN) learn to generate personalized and fake-but-realistic rankings of preferences for top-N recommendations by using p…
Collaborative FilteringAttention-Based Recommendation On Graphs
Graph Neural Networks (GNN) have shown remarkable performance in different tasks. However, there are a few studies about GNN on recommender systems. GCN as a type of GNNs can extract high-quality embeddings for different…
Collaborative FilteringRecommendation SystemsPedagogical Word Recommendation: A novel task and dataset on personalized vocabulary acquisition for L2 learners
When learning a second language (L2), one of the most important but tedious components that often demoralizes students with its ineffectiveness and inefficiency is vocabulary acquisition, or more simply put, memorizing w…
Collaborative FilteringComprehensive Movie Recommendation System
A recommender system, also known as a recommendation system, is a type of information filtering system that attempts to forecast a user's rating or preference for an item. This article designs and implements a complete m…
Collaborative FilteringMovie RecommendationRecommendation SystemsTAGLECF: Recommendation via Learnable Edge Collaborative Filtering
The core of recommendation models is estimating the probability that a user will like an item based on historical interactions. Existing collaborative filtering (CF) algorithms compute the likelihood by utilizing simple …
Collaborative FilteringMovie Recommender System using critic consensus
Recommendation systems are perhaps one of the most important agents for industry growth through the modern Internet world. Previous approaches on recommendation systems include collaborative filtering and content based f…
Collaborative FilteringRecommendation Systems