Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Simulation comparisons between Bayesian and de-biased estimators in low-rank matrix completion
In this paper, we study the low-rank matrix completion problem, a class of machine learning problems, that aims at the prediction of missing entries in a partially observed matrix. Such problems appear in several challen…
Collaborative FilteringImputationLow-Rank Matrix CompletionMatrix CompletionAdversarial and Contrastive Variational Autoencoder for Sequential Recommendation
Sequential recommendation as an emerging topic has attracted increasing attention due to its important practical significance. Models based on deep learning and attention mechanism have achieved good performance in seque…
Collaborative FilteringSequential RecommendationCollaborative Filtering Approach to Link Prediction
Link prediction is a fundamental challenge in network science. Among various methods, local similarity indices are widely used for their high cost-performance. However, the performance is less robust: for some networks l…
Collaborative FilteringLink PredictionPredictionRecommendation SystemsRecommending Short-lived Dynamic Packages for Golf Booking Services
We introduce an approach to recommending short-lived dynamic packages for golf booking services. Two challenges are addressed in this work. The first is the short life of the items, which puts the system in a state of a …
ClusteringCollaborative FilteringRecommendation SystemsLarge-scale Recommendation for Portfolio Optimization
Individual investors are now massively using online brokers to trade stocks with convenient interfaces and low fees, albeit losing the advice and personalization traditionally provided by full-service brokers. We frame t…
Collaborative FilteringManagementPortfolio OptimizationFES: A Fast Efficient Scalable QoS Prediction Framework
Quality-of-Service prediction of web service is an integral part of services computing due to its diverse applications in the various facets of a service life cycle, such as service composition, service selection, servic…
Collaborative FilteringPredictionService CompositionBCFNet: A Balanced Collaborative Filtering Network with Attention Mechanism
Collaborative Filtering (CF) based recommendation methods have been widely studied, which can be generally categorized into two types, i.e., representation learning-based CF methods and matching function learning-based C…
Collaborative FilteringRepresentation Learningu-cf2vec: Representation Learning for Personalized Algorithm Selection in Recommender Systems
Collaborative Filtering (CF) has become the standard approach to solve recommendation systems (RS) problems. Collaborative Filtering algorithms try to make predictions about interests of a user by collecting the personal…
Collaborative FilteringMeta-LearningRecommendation SystemsRepresentation LearningPopularity-Opportunity Bias in Collaborative Filtering
This paper connects equal opportunity to popularity bias in implicit recommenders to introduce the problem of popularity-opportunity bias. That is, conditioned on user preferences that a user likes both items, the more p…
Collaborative FilteringGraph Convolutional Embeddings for Recommender Systems
Modern recommender systems (RS) work by processing a number of signals that can be inferred from large sets of user-item interaction data. The main signal to analyze stems from the raw matrix that represents interactions…
Collaborative FilteringRecommendation SystemsExplore User Neighborhood for Real-time E-commerce Recommendation
Recommender systems play a vital role in modern online services, such as Amazon and Taobao. Traditional personalized methods, which focus on user-item (UI) relations, have been widely applied in industrial settings, owin…
Collaborative FilteringRecommendation SystemsHistory-Augmented Collaborative Filtering for Financial Recommendations
In many businesses, and particularly in finance, the behavior of a client might drastically change over time. It is consequently crucial for recommender systems used in such environments to be able to adapt to these chan…
Collaborative FilteringRecommendation SystemsNeural content-aware collaborative filtering for cold-start music recommendation
State-of-the-art music recommender systems are based on collaborative filtering, which builds upon learning similarities between users and songs from the available listening data. These approaches inherently face the col…
Collaborative FilteringMusic RecommendationRecommendation SystemsFeature-level Attentive ICF for Recommendation
Item-based collaborative filtering (ICF) enjoys the advantages of high recommendation accuracy and ease in online penalization and thus is favored by the industrial recommender systems. ICF recommends items to a target u…
Collaborative FilteringRecommendation SystemsLink Prediction Approach to Recommender Systems
The problem of recommender system is very popular with myriad available solutions. A novel approach that uses the link prediction problem in social networks has been proposed in the literature that model the typical user…
Binary ClassificationCollaborative FilteringLink PredictionPrediction+1User Embedding based Neighborhood Aggregation Method for Inductive Recommendation
We consider the problem of learning latent features (aka embedding) for users and items in a recommendation setting. Given only a user-item interaction graph, the goal is to recommend items for each user. Traditional app…
Collaborative FilteringUserReg: A Simple but Strong Model for Rating Prediction
Collaborative filtering (CF) has achieved great success in the field of recommender systems. In recent years, many novel CF models, particularly those based on deep learning or graph techniques, have been proposed for a …
Collaborative FilteringPredictionRecommendation SystemsKnowledgeCheckR: Intelligent Techniques for Counteracting Forgetting
Existing e-learning environments primarily focus on the aspect of providing intuitive learning contents and to recommend learning units in a personalized fashion. The major focus of the KnowledgeCheckR environment is to …
Collaborative FilteringQuestion AnsweringSession-Based RecommendationsSceneRec: Scene-Based Graph Neural Networks for Recommender Systems
Collaborative filtering has been largely used to advance modern recommender systems to predict user preference. A key component in collaborative filtering is representation learning, which aims to project users and items…
Collaborative FilteringRecommendation SystemsRepresentation LearningDrug Package Recommendation via Interaction-aware Graph Induction
Recent years have witnessed the rapid accumulation of massive electronic medical records (EMRs), which highly support the intelligent medical services such as drug recommendation. However, prior arts mainly follow the tr…
AttributeCollaborative Filtering