Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering
Graph neural networks (GNNs) have been widely applied in the recommendation tasks and have obtained very appealing performance. However, most GNN-based recommendation methods suffer from the problem of data sparsity in p…
Collaborative FilteringDeep Attentional Guided Image Filtering
Guided filter is a fundamental tool in computer vision and computer graphics which aims to transfer structure information from guidance image to target image. Most existing methods construct filter kernels from the guida…
Collaborative FilteringDepth Image UpsamplingDepth Map Super-ResolutionCold Item Integration in Deep Hybrid Recommenders via Tunable Stochastic Gates
A major challenge in collaborative filtering methods is how to produce recommendations for cold items (items with no ratings), or integrate cold item into an existing catalog. Over the years, a variety of hybrid recommen…
ArticlesCollaborative FilteringZero-Shot Recommendation as Language Modeling
Recommendation is the task of ranking items (e.g. movies or products) according to individual user needs. Current systems rely on collaborative filtering and content-based techniques, which both require structured traini…
Collaborative FilteringLanguage ModelingLanguage ModellingGiven Users Recommendations Based on Reviews on Yelp
In our project, we focus on NLP-based hybrid recommendation systems. Our data is from Yelp Data. For our hybrid recommendation system, we have two major components: the first part is to embed the reviews with the Bert mo…
Collaborative FilteringRecommendation SystemsCombinations of Jaccard with Numerical Measures for Collaborative Filtering Enhancement: Current Work and Future Proposal
Collaborative filtering (CF) is an important approach for recommendation system which is widely used in a great number of aspects of our life, heavily in the online-based commercial systems. One popular algorithms in CF …
Collaborative FilteringMitigating Divergence of Latent Factors via Dual Ascent for Low Latency Event Prediction Models
Real-world content recommendation marketplaces exhibit certain behaviors and are imposed by constraints that are not always apparent in common static offline data sets. One example that is common in ad marketplaces is sw…
Collaborative FilteringEvolving Deep Neural Networks for Collaborative Filtering
Collaborative Filtering (CF) is widely used in recommender systems to model user-item interactions. With the great success of Deep Neural Networks (DNNs) in various fields, advanced works recently have proposed several D…
Collaborative FilteringRecommendation SystemsLinear, or Non-Linear, That is the Question!
There were fierce debates on whether the non-linear embedding propagation of GCNs is appropriate to GCN-based recommender systems. It was recently found that the linear embedding propagation shows better accuracy than th…
Collaborative FilteringRecommendation SystemsAmazon SageMaker Model Parallelism: A General and Flexible Framework for Large Model Training
With deep learning models rapidly growing in size, systems-level solutions for large-model training are required. We present Amazon SageMaker model parallelism, a software library that integrates with PyTorch, and enable…
Collaborative FilteringmodelLatent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation
Recent years have witnessed growing interests in multimedia recommendation, which aims to predict whether a user will interact with an item with multimodal contents. Previous studies focus on modeling user-item interacti…
Collaborative FilteringMultimedia recommendationUltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation
With the recent success of graph convolutional networks (GCNs), they have been widely applied for recommendation, and achieved impressive performance gains. The core of GCNs lies in its message passing mechanism to aggre…
Collaborative FilteringRecommendation SystemsRevisiting the Performance of iALS on Item Recommendation Benchmarks
Matrix factorization learned by implicit alternating least squares (iALS) is a popular baseline in recommender system research publications. iALS is known to be one of the most computationally efficient and scalable coll…
Collaborative FilteringRecommendation SystemsContent Filtering Enriched GNN Framework for News Recommendation
Learning accurate users and news representations is critical for news recommendation. Despite great progress, existing methods seem to have a strong bias towards content representation or just capture collaborative filte…
Collaborative FilteringNews RecommendationRethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives
The recent work by Rendle et al. (2020), based on empirical observations, argues that matrix-factorization collaborative filtering (MCF) compares favorably to neural collaborative filtering (NCF), and conjectures the dot…
Collaborative FilteringTransductive LearningImplicit Feedbacks are Not Always Favorable: Iterative Relabeled One-Class Collaborative Filtering against Noisy Interactions
Due to privacy concerns, there is a rising favor in Recommender System community for the One-class Collaborative Filtering (OCCF) framework, which predicts user preferences only based on binary implicit feedback (e.g., c…
Collaborative FilteringRecommendation SystemsDeconfounded Causal Collaborative Filtering
Recommender systems may be confounded by various types of confounding factors (also called confounders) that may lead to inaccurate recommendations and sacrificed recommendation performance. Current approaches to solving…
Collaborative FilteringRecommendation SystemsControllable Recommenders using Deep Generative Models and Disentanglement
In this paper, we consider controllability as a means to satisfy dynamic preferences of users, enabling them to control recommendations such that their current preference is met. While deep models have shown improved per…
Collaborative FilteringDisentanglementKnowledge-aware Coupled Graph Neural Network for Social Recommendation
Social recommendation task aims to predict users' preferences over items with the incorporation of social connections among users, so as to alleviate the sparse issue of collaborative filtering. While many recent efforts…
Collaborative FilteringGraph Neural NetworkRecommendation SystemsSocial Recommendation with Self-Supervised Metagraph Informax Network
In recent years, researchers attempt to utilize online social information to alleviate data sparsity for collaborative filtering, based on the rationale that social networks offers the insights to understand the behavior…
Collaborative FilteringGraph Neural NetworkRecommendation Systems