Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
One-Bit Matrix Completion with Differential Privacy
As a prevailing collaborative filtering method for recommendation systems, one-bit matrix completion requires data collected by users to provide personalized service. Due to insidious attacks and unexpected inference, th…
Collaborative FilteringMatrix CompletionPrivacy PreservingRecommendation SystemsIncorporating User-Item Similarity in Hybrid Neighborhood-based Recommendation System
Modern hybrid recommendation systems require a sufficient amount of data. However, several internet privacy issues make users skeptical about sharing their personal information with online service providers. This work in…
Collaborative FilteringRecommendation SystemsNeuron-Enhanced Autoencoder based Collaborative filtering: Theory and Practice
This paper presents a novel recommendation method called neuron-enhanced autoencoder based collaborative filtering (NE-AECF). The method uses an additional neural network to enhance the reconstruction capability of autoe…
Collaborative FilteringA Multi-Task Learning Algorithm for Non-personalized Recommendations
In this paper, we introduce a multi-task learning (MTL) algorithm for recommending non-personalized videos to watch next on industrial video sharing platforms. Personalized recommendations have been studied for decades, …
Collaborative FilteringMulti-Task LearningRecommendation SystemsExtracting Attentive Social Temporal Excitation for Sequential Recommendation
In collaborative filtering, it is an important way to make full use of social information to improve the recommendation quality, which has been proved to be effective because user behavior will be affected by her friends…
Collaborative FilteringGraph EmbeddingPoint ProcessesSequential RecommendationBoost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction Prediction
Despite experimental and curation efforts, the extent of enzyme promiscuity on substrates continues to be largely unexplored and under documented. Recommender systems (RS), which are currently unexplored for the enzyme-s…
AttributeAuxiliary LearningCollaborative FilteringContrastive Learning+2Sparse Plus Low Rank Matrix Decomposition: A Discrete Optimization Approach
We study the Sparse Plus Low-Rank decomposition problem (SLR), which is the problem of decomposing a corrupted data matrix into a sparse matrix of perturbations plus a low-rank matrix containing the ground truth. SLR is …
Collaborative FilteringData CompressionSimpleX: A Simple and Strong Baseline for Collaborative Filtering
Collaborative filtering (CF) is a widely studied research topic in recommender systems. The learning of a CF model generally depends on three major components, namely interaction encoder, loss function, and negative samp…
Collaborative FilteringRecommendation SystemsPareto Optimality for Fairness-constrained Collaborative Filtering
The well-known collaborative filtering (CF) models typically optimize a single objective summed over all historical user-item interactions. Due to inevitable imbalances and biases in real-world data, they may develop a p…
Collaborative FilteringFairnessRecovering individual emotional states from sparse ratings using collaborative filtering
A fundamental challenge in emotion research is measuring feeling states with high granularity and temporal precision without disrupting the emotion generation process. Here we introduce and validate a new approach in whi…
Collaborative FilteringImputationFast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filtering
Variational AutoEncoder (VAE) has been extended as a representative nonlinear method for collaborative filtering. However, the bottleneck of VAE lies in the softmax computation over all items, such that it takes linear c…
Collaborative FilteringMatrix Factorization for Collaborative Filtering Is Just Solving an Adjoint Latent Dirichlet Allocation Model After All
Matrix factorization-based methods are among the most popular methods for collaborative filtering tasks with implicit feedback. The most effective of these methods do not apply sign constraints, such as non-negativity, t…
AllCollaborative FilteringAn Improved Hybrid Recommender System: Integrating Document Context-Based and Behavior-Based Methods
One of the main challenges in recommender systems is data sparsity which leads to high variance. Several attempts have been made to improve the bias-variance trade-off using auxiliary information. In particular, document…
Collaborative FilteringRecommendation SystemsUsing Collaborative Filtering to Model Argument Selection
This study evaluates whether model-based Collaborative Filtering (CF) algorithms, which have been extensively studied and widely used to build recommender systems, can be used to predict which common nouns a predicate ca…
Collaborative FilteringmodelRecommendation SystemsGLocal-K: Global and Local Kernels for Recommender Systems
Recommender systems typically operate on high-dimensional sparse user-item matrices. Matrix completion is a very challenging task to predict one's interest based on millions of other users having each seen a small subset…
Collaborative FilteringMatrix CompletionRecommendation SystemsPhotos Are All You Need for Reciprocal Recommendation in Online Dating
Recommender Systems are algorithms that predict a user's preference for an item. Reciprocal Recommenders are a subset of recommender systems, where the items in question are people, and the objective is therefore to pred…
AllCollaborative FilteringRecommendation SystemsA Recommender System for Scientific Datasets and Analysis Pipelines
Scientific datasets and analysis pipelines are increasingly being shared publicly in the interest of open science. However, mechanisms are lacking to reliably identify which pipelines and datasets can appropriately be us…
Collaborative FilteringRecommendation SystemsHow Powerful is Graph Convolution for Recommendation?
Graph convolutional networks (GCNs) have recently enabled a popular class of algorithms for collaborative filtering (CF). Nevertheless, the theoretical underpinnings of their empirical successes remain elusive. In this p…
Collaborative FilteringWhen Product Search Meets Collaborative Filtering: A Hierarchical Heterogeneous Graph Neural Network Approach
Personalization lies at the core of boosting the product search system performance. Prior studies mainly resorted to the semantic matching between textual queries and user/product related documents, leaving the user coll…
Collaborative FilteringGraph Neural NetworkRepresentation LearningSentenceDGCN: Diversified Recommendation with Graph Convolutional Networks
These years much effort has been devoted to improving the accuracy or relevance of the recommendation system. Diversity, a crucial factor which measures the dissimilarity among the recommended items, received rather litt…
Collaborative FilteringDiversity