Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering
Graph Neural Networks (GNNs) have recently been utilized to build Collaborative Filtering (CF) models to predict user preferences based on historical user-item interactions. However, there is relatively little understand…
Collaborative FilteringMulti-modal RecommendationRecommendation SystemsRepresentation LearningTransformer-Empowered Content-Aware Collaborative Filtering
Knowledge graph (KG) based Collaborative Filtering is an effective approach to personalizing recommendation systems for relatively static domains such as movies and books, by leveraging structured information from KG to …
Collaborative FilteringContrastive LearningRecommendation SystemsBayesian Negative Sampling for Recommendation
How to sample high quality negative instances from unlabeled data, i.e., negative sampling, is important for training implicit collaborative filtering and contrastive learning models. Although previous studies have propo…
Collaborative FilteringContrastive LearningInformativenessRevisiting Neighborhood-based Link Prediction for Collaborative Filtering
Collaborative filtering (CF) is one of the most successful and fundamental techniques in recommendation systems. In recent years, Graph Neural Network (GNN)-based CF models, such as NGCF [31], LightGCN [10] and GTN [9] h…
Collaborative FilteringGraph Neural NetworkLink PredictionPrediction+1Transfer of codebook latent factors for cross-domain recommendation with non-overlapping data
Recommender systems based on collaborative filtering play a vital role in many E-commerce applications as they guide the user in finding their items of interest based on the user's past transactions and feedback of other…
Collaborative FilteringRecommendation SystemsTransfer LearningMaking Recommender Systems Forget: Learning and Unlearning for Erasable Recommendation
Privacy laws and regulations enforce data-driven systems, e.g., recommender systems, to erase the data that concern individuals. As machine learning models potentially memorize the training data, data erasure should also…
Collaborative FilteringMachine UnlearningRecommendation SystemsARERec: Attentive Local Interaction Model for Sequential Recommendation
Previous sequential-recommendation methods have been able to capture patterns of item characteristics that interact with the user. However, they modeled user behavior using a whole interaction sequence, despite possible …
Collaborative FilteringmodelRecommendation SystemsSequential RecommendationLearning Expanding Graphs for Signal Interpolation
Performing signal processing over graphs requires knowledge of the underlying fixed topology. However, graphs often grow in size with new nodes appearing over time, whose connectivity is typically unknown; hence, making …
Collaborative FilteringDistributed-Memory Sparse Kernels for Machine Learning
Sampled Dense Times Dense Matrix Multiplication (SDDMM) and Sparse Times Dense Matrix Multiplication (SpMM) appear in diverse settings, such as collaborative filtering, document clustering, and graph embedding. Frequentl…
BIG-bench Machine LearningCollaborative FilteringGraph EmbeddingSimultaneous Learning of the Inputs and Parameters in Neural Collaborative Filtering
Neural network-based collaborative filtering systems focus on designing network architectures to learn better representations while fixing the input to the user/item interaction vectors and/or ID. In this paper, we first…
Collaborative FilteringAn Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering
Recommender systems are information retrieval methods that predict user preferences to personalize services. These systems use the feedback and the ratings provided by users to model the behavior of users and to generate…
Active LearningCollaborative FilteringInformation RetrievalRecommendation Systems+1Recommendations in a Multi-Domain Setting: Adapting for Customization, Scalability and Real-Time Performance
In this industry talk at ECIR'2022, we illustrate how to build a modern recommender system that can serve recommendations in real-time for a diverse set of application domains. Specifically, we present our system archite…
Collaborative FilteringMulti-Domain Recommender SystemsRecommendation SystemsTop-N Recommendation Algorithms: A Quest for the State-of-the-Art
Research on recommender systems algorithms, like other areas of applied machine learning, is largely dominated by efforts to improve the state-of-the-art, typically in terms of accuracy measures. Several recent research …
Collaborative FilteringRecommendation SystemsPopularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems
Multimedia recommender systems suggest media items, e.g., songs, (digital) books and movies, to users by utilizing concepts of traditional recommender systems such as collaborative filtering. In this paper, we investigat…
Collaborative FilteringRecommendation SystemsWSLRec: Weakly Supervised Learning for Neural Sequential Recommendation Models
Learning the user-item relevance hidden in implicit feedback data plays an important role in modern recommender systems. Neural sequential recommendation models, which formulates learning the user-item relevance as a seq…
Collaborative FilteringRecommendation SystemsSequential RecommendationWeakly-supervised LearningConsensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering
Over the past decades, for One-Class Collaborative Filtering (OCCF), many learning objectives have been researched based on a variety of underlying probabilistic models. From our analysis, we observe that models trained …
Collaborative FilteringTEE-based decentralized recommender systems: The raw data sharing redemption
Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between user profiles to make recommendations, but…
Collaborative FilteringFederated LearningRecommendation SystemsImproving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning
Recently, graph collaborative filtering methods have been proposed as an effective recommendation approach, which can capture users' preference over items by modeling the user-item interaction graphs. In order to reduce …
Collaborative FilteringContrastive LearningA Tech Hybrid-Recommendation Engine and Personalized Notification: An integrated tool to assist users through Recommendations (Project ATHENA)
Project ATHENA aims to develop an application to address information overload, primarily focused on Recommendation Systems (RSs) with the personalization and user experience design of a modern system. Two machine learnin…
Collaborative FilteringRecommendation SystemsCollaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation
Cross-Domain Recommendation (CDR) has been popularly studied to utilize different domain knowledge to solve the data sparsity and cold-start problem in recommender systems. In this paper, we focus on the Review-based Non…
Collaborative FilteringRecommendation Systems