Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
In recent years, there are a large number of recommendation algorithms proposed in the literature, from traditional collaborative filtering to deep learning algorithms. However, the concerns about how to standardize open…
Collaborative FilteringGPURecommendation SystemsSequential RecommendationSJTU-NICT’s Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task
In this paper, we introduced our joint team SJTU-NICT ‘s participation in the WMT 2020 machine translation shared task. In this shared task, we participated in four translation directions of three language pairs: English…
Collaborative FilteringLanguage ModelingLanguage ModellingMachine Translation+3Attentive Autoencoders for Multifaceted Preference Learning in One-class Collaborative Filtering
Most existing One-Class Collaborative Filtering (OC-CF) algorithms estimate a user's preference as a latent vector by encoding their historical interactions. However, users often show diverse interests, which significant…
Collaborative FilteringRecommendation SystemsNGAT4Rec: Neighbor-Aware Graph Attention Network For Recommendation
Learning informative representations (aka. embeddings) of users and items is the core of modern recommender systems. Previous works exploit user-item relationships of one-hop neighbors in the user-item interaction graph …
Collaborative FilteringGraph AttentionGraph EmbeddingGraph Neural Network+1A Critical Study on Data Leakage in Recommender System Offline Evaluation
Recommender models are hard to evaluate, particularly under offline setting. In this paper, we provide a comprehensive and critical analysis of the data leakage issue in recommender system offline evaluation. Data leakag…
Collaborative FilteringRecommendation SystemsLearning to Embed Categorical Features without Embedding Tables for Recommendation
Embedding learning of categorical features (e.g. user/item IDs) is at the core of various recommendation models including matrix factorization and neural collaborative filtering. The standard approach creates an embeddin…
Collaborative FilteringNatural Language UnderstandingNetwork EmbeddingRecommendation SystemsSelf-supervised Graph Learning for Recommendation
Representation learning on user-item graph for recommendation has evolved from using single ID or interaction history to exploiting higher-order neighbors. This leads to the success of graph convolution networks (GCNs) f…
Collaborative FilteringGraph LearningRepresentation LearningSelf-Supervised LearningLeveraging the structure of musical preference in content-aware music recommendation
State-of-the-art music recommendation systems are based on collaborative filtering, which predicts a user's interest from his listening habits and similarities with other users' profiles. These approaches are agnostic to…
Collaborative FilteringMusic RecommendationRecommendation SystemsOn Application of Block Kaczmarz Methods in Matrix Factorization
Matrix factorization techniques compute low-rank product approximations of high dimensional data matrices and as a result, are often employed in recommender systems and collaborative filtering applications. However, many…
Collaborative FilteringRecommendation SystemsA Unified Model for Recommendation with Selective Neighborhood Modeling
Neighborhood-based recommenders are a major class of Collaborative Filtering (CF) models. The intuition is to exploit neighbors with similar preferences for bridging unseen user-item pairs and alleviating data sparseness…
Collaborative FilteringHierarchical Text Interaction for Rating Prediction
Traditional recommender systems encounter several challenges such as data sparsity and unexplained recommendation. To address these challenges, many works propose to exploit semantic information from review data. However…
Collaborative FilteringPredictionRecommendation SystemsTemporal Collaborative Filtering with Graph Convolutional Neural Networks
Temporal collaborative filtering (TCF) methods aim at modelling non-static aspects behind recommender systems, such as the dynamics in users' preferences and social trends around items. State-of-the-art TCF methods emplo…
Collaborative FilteringGraph Neural NetworkRecommendation SystemsModurec: Recommender Systems with Feature and Time Modulation
Current state of the art algorithms for recommender systems are mainly based on collaborative filtering, which exploits user ratings to discover latent factors in the data. These algorithms unfortunately do not make effe…
Collaborative FilteringRecommendation SystemsSJTU-NICT's Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task
In this paper, we introduced our joint team SJTU-NICT 's participation in the WMT 2020 machine translation shared task. In this shared task, we participated in four translation directions of three language pairs: English…
Collaborative FilteringLanguage ModelingLanguage ModellingMachine Translation+3Social movie recommender system based on deep autoencoder network using Twitter data
Recommender systems attempt to provide effective suggestions to each user based on their interests and behaviors. These recommendations usually match the personal user preferences and assist them in the decision-making p…
Collaborative FilteringDecision MakingRecommendation SystemsA Light Heterogeneous Graph Collaborative Filtering Model using Textual Information
Due to the development of graph neural networks, graph-based representation learning methods have made great progress in recommender systems. However, data sparsity is still a challenging problem that most graph-based re…
Collaborative FilteringRecommendation SystemsRepresentation LearningLinear-Sample Learning of Low-Rank Distributions
Many latent-variable applications, including community detection, collaborative filtering, genomic analysis, and NLP, model data as generated by low-rank matrices. Yet despite considerable research, except for very speci…
Collaborative FilteringCommunity DetectionSimultaneous Relevance and Diversity: A New Recommendation Inference Approach
Relevance and diversity are both important to the success of recommender systems, as they help users to discover from a large pool of items a compact set of candidates that are not only interesting but exploratory as wel…
Collaborative FilteringDiversityRecommendation SystemsExplainable Recommendations via Attentive Multi-Persona Collaborative Filtering
Two main challenges in recommender systems are modeling users with heterogeneous taste, and providing explainable recommendations. In this paper, we propose the neural Attentive Multi-Persona Collaborative Filtering (AMP…
Collaborative FilteringRecommendation SystemsLearning Representations of Hierarchical Slates in Collaborative Filtering
We are interested in building collaborative filtering models for recommendation systems where users interact with slates instead of individual items. These slates can be hierarchical in nature. The central idea of our ap…
Collaborative FilteringRecommendation SystemsRepresentation Learning