Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Contemporary Recommendation Systems on Big Data and Their Applications: A Survey
This survey paper conducts a comprehensive analysis of the evolution and contemporary landscape of recommendation systems, which have been extensively incorporated across a myriad of web applications. It delves into the …
Collaborative FilteringRecommendation SystemsSurveyCGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning
Graph similarity learning refers to calculating the similarity score between two graphs, which is required in many realistic applications, such as visual tracking, graph classification, and collaborative filtering. As mo…
Collaborative FilteringGraph ClassificationGraph MatchingGraph Similarity+2HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation
The ever-increasing data scale of user-item interactions makes it challenging for an effective and efficient recommender system. Recently, hash-based collaborative filtering (Hash-CF) approaches employ efficient Hamming …
Collaborative FilteringRecommendation SystemsRecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation
Recipe recommendation systems play an essential role in helping people decide what to eat. Existing recipe recommendation systems typically focused on content-based or collaborative filtering approaches, ignoring the hig…
Collaborative FilteringContrastive LearningGraph LearningGraph Neural Network+1GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation
Generating recommendations based on user-item interactions and user-user social relations is a common use case in web-based systems. These connections can be naturally represented as graph-structured data and thus utiliz…
Collaborative FilteringPAS: A Position-Aware Similarity Measurement for Sequential Recommendation
The common item-based collaborative filtering framework becomes a typical recommendation method when equipped with a certain item-to-item similarity measurement. On one hand, we realize that a well-designed similarity me…
Collaborative FilteringPositionRecommendation SystemsSequential RecommendationTensor-based Collaborative Filtering With Smooth Ratings Scale
Conventional collaborative filtering techniques don't take into consideration the effect of discrepancy in users' rating perception. Some users may rarely give 5 stars to items while others almost always assign 5 stars t…
Collaborative FilteringRecommendation SystemsAre Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks
Collaborative filtering models generally perform better than content-based filtering models and do not require careful feature engineering. However, in the cold-start scenario collaborative information may be scarce or e…
Collaborative FilteringFeature Engineeringfeature selectionRecommendation Systems+1FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation
Non-negative matrix factorization (NMF) with missing-value completion is a well-known effective Collaborative Filtering (CF) method used to provide personalized user recommendations. However, traditional CF relies on the…
Collaborative FilteringFederated LearningKnowledge DistillationPrivacy PreservingA Review on Pushing the Limits of Baseline Recommendation Systems with the integration of Opinion Mining & Information Retrieval Techniques
Recommendations Systems allow users to identify trending items among a community while being timely and relevant to the user's expectations. When the purpose of various Recommendation Systems differs, the required type o…
Collaborative FilteringInformation RetrievalOpinion MiningRecommendation Systems+1Explain and Conquer: Personalised Text-based Reviews to Achieve Transparency
There are many contexts in which dyadic data are present. Social networks are a well-known example. In these contexts, pairs of elements are linked building a network that reflects interactions. Explaining why these rela…
Collaborative FilteringLanguage ModellingLearning-To-RankNatural Language Understanding+1MovieMat: Context-aware Movie Recommendation with Matrix Factorization by Matrix Fitting
Movie Recommender System is widely applied in commercial environments such as NetFlix and Tubi. Classic recommender models utilize technologies such as collaborative filtering, learning to rank, matrix factorization and …
Collaborative FilteringFairnessLearning-To-RankMarketing+2Hypergraph Contrastive Collaborative Filtering
Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing users and items into latent representation space, with their correlative patterns from interaction data. Among various CF techniques, t…
Collaborative FilteringContrastive LearningRecommendation SystemsSelf-Supervised LearningInvestigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering
Recent years have witnessed the great accuracy performance of graph-based Collaborative Filtering (CF) models for recommender systems. By taking the user-item interaction behavior as a graph, these graph-based CF models …
Collaborative FilteringRecommendation SystemsTrading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach
Collaborative filtering (CF), as a standard method for recommendation with implicit feedback, tackles a semi-supervised learning problem where most interaction data are unobserved. Such a nature makes existing approaches…
Collaborative FilteringLess is More: Reweighting Important Spectral Graph Features for Recommendation
As much as Graph Convolutional Networks (GCNs) have shown tremendous success in recommender systems and collaborative filtering (CF), the mechanism of how they, especially the core components (\textit{i.e.,} neighborhood…
Collaborative FilteringDenoisingRecommendation SystemsBroad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach
Recently, Deep Neural Networks (DNNs) have been widely introduced into Collaborative Filtering (CF) to produce more accurate recommendation results due to their capability of capturing the complex nonlinear relationships…
Collaborative FilteringRecommendation SystemsHRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization
In large-scale recommender systems, the user-item networks are generally scale-free or expand exponentially. The latent features (also known as embeddings) used to describe the user and item are determined by how well th…
Collaborative FilteringRecommendation SystemsGRAM: Fast Fine-tuning of Pre-trained Language Models for Content-based Collaborative Filtering
Content-based collaborative filtering (CCF) predicts user-item interactions based on both users' interaction history and items' content information. Recently, pre-trained language models (PLM) have been used to extract h…
Collaborative FilteringGPUKnowledge TracingNews RecommendationIA-GCN: Interactive Graph Convolutional Network for Recommendation
Recently, Graph Convolutional Network (GCN) has become a novel state-of-art for Collaborative Filtering (CF) based Recommender Systems (RS). It is a common practice to learn informative user and item representations by p…
Collaborative FilteringRecommendation Systems