Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems
Graph Neural Networks (GNNs) have emerged as promising solutions for collaborative filtering (CF) through the modeling of user-item interaction graphs. The nucleus of existing GNN-based recommender systems involves recur…
Collaborative FilteringGraph ClassificationGraph Representation LearningRecommendation Systems+1Network-Based Video Recommendation Using Viewing Patterns and Modularity Analysis: An Integrated Framework
The proliferation of video-on-demand (VOD) services has led to a paradox of choice, overwhelming users with vast content libraries and revealing limitations in current recommender systems. This research introduces a nove…
ClusteringCollaborative FilteringRecommendation SystemsVideo SimilarityHow Expressive are Graph Neural Networks in Recommendation?
Graph Neural Networks (GNNs) have demonstrated superior performance on various graph learning tasks, including recommendation, where they leverage user-item collaborative filtering signals in graphs. However, theoretical…
Collaborative FilteringGraph LearningA Topology-aware Analysis of Graph Collaborative Filtering
The successful integration of graph neural networks into recommender systems (RSs) has led to a novel paradigm in collaborative filtering (CF), graph collaborative filtering (graph CF). By representing user-item data as …
Collaborative FilteringGraph SamplingRecommendation SystemsAdversarial Collaborative Filtering for Free
Collaborative Filtering (CF) has been successfully used to help users discover the items of interest. Nevertheless, existing CF methods suffer from noisy data issue, which negatively impacts the quality of recommendation…
Collaborative FilteringGraph-based Alignment and Uniformity for Recommendation
Collaborative filtering-based recommender systems (RecSys) rely on learning representations for users and items to predict preferences accurately. Representation learning on the hypersphere is a promising approach due to…
Collaborative FilteringRecommendation SystemsRepresentation LearningA Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems
As the focus on Large Language Models (LLMs) in the field of recommendation intensifies, the optimization of LLMs for recommendation purposes (referred to as LLM4Rec) assumes a crucial role in augmenting their effectiven…
Collaborative FilteringRecommendation SystemsSelf-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph
Knowledge graphs (KGs) are commonly used as side information to enhance collaborative signals and improve recommendation quality. In the context of knowledge-aware recommendation (KGR), graph neural networks (GNNs) have …
Collaborative FilteringKnowledge-Aware RecommendationKnowledge GraphsSelf-Supervised LearningCross-Attribute Matrix Factorization Model with Shared User Embedding
Over the past few years, deep learning has firmly established its prowess across various domains, including computer vision, speech recognition, and natural language processing. Motivated by its outstanding success, rese…
AttributeCollaborative FilteringRecommendation Systemsspeech-recognition+1MM-GEF: Multi-modal representation meet collaborative filtering
In modern e-commerce, item content features in various modalities offer accurate yet comprehensive information to recommender systems. The majority of previous work either focuses on learning effective item representatio…
Collaborative FilteringMulti-modal RecommendationRecommendation SystemsUIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering
Recommending items to potentially interested users has been an important commercial task that faces two main challenges: accuracy and explainability. While most collaborative filtering models rely on statistical computat…
Collaborative FilteringAutoSeqRec: Autoencoder for Efficient Sequential Recommendation
Sequential recommendation demonstrates the capability to recommend items by modeling the sequential behavior of users. Traditional methods typically treat users as sequences of items, overlooking the collaborative relati…
Collaborative FilteringComputational EfficiencySequential RecommendationToward a Better Understanding of Loss Functions for Collaborative Filtering
Collaborative filtering (CF) is a pivotal technique in modern recommender systems. The learning process of CF models typically consists of three components: interaction encoder, loss function, and negative sampling. Alth…
Collaborative FilteringRecommendation SystemsAugmented Negative Sampling for Collaborative Filtering
Negative sampling is essential for implicit-feedback-based collaborative filtering, which is used to constitute negative signals from massive unlabeled data to guide supervised learning. The state-of-the-art idea is to u…
Collaborative FilteringTopic-Level Bayesian Surprise and Serendipity for Recommender Systems
A recommender system that optimizes its recommendations solely to fit a user's history of ratings for consumed items can create a filter bubble, wherein the user does not get to experience items from novel, unseen catego…
Collaborative FilteringRecommendation SystemsSSLRec: A Self-Supervised Learning Framework for Recommendation
Self-supervised learning (SSL) has gained significant interest in recent years as a solution to address the challenges posed by sparse and noisy data in recommender systems. Despite the growing number of SSL algorithms d…
Collaborative FilteringData AugmentationRecommendation SystemsSelf-Supervised Learning+1Unified Matrix Factorization with Dynamic Multi-view Clustering
Matrix factorization (MF) is a classical collaborative filtering algorithm for recommender systems. It decomposes the user-item interaction matrix into a product of low-dimensional user representation matrix and item rep…
ClusteringCollaborative FilteringRecommendation SystemsUniRecSys: A Unified Framework for Personalized, Group, Package, and Package-to-Group Recommendations
Recommender systems aim to enhance the overall user experience by providing tailored recommendations for a variety of products and services. These systems help users make more informed decisions, leading to greater user …
Collaborative FilteringRecommendation SystemsOnline Distillation-enhanced Multi-modal Transformer for Sequential Recommendation
Multi-modal recommendation systems, which integrate diverse types of information, have gained widespread attention in recent years. However, compared to traditional collaborative filtering-based multi-modal recommendatio…
Collaborative FilteringMulti-modal RecommendationRecommendation SystemsRepresentation Learning+1RECipe: Does a Multi-Modal Recipe Knowledge Graph Fit a Multi-Purpose Recommendation System?
Over the past two decades, recommendation systems (RSs) have used machine learning (ML) solutions to recommend items, e.g., movies, books, and restaurants, to clients of a business or an online platform. Recipe recommend…
BenchmarkingCollaborative FilteringGraph EmbeddingKnowledge Graph Embedding+3