Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Explaining Latent Factor Models for Recommendation with Influence Functions
Latent factor models (LFMs) such as matrix factorization achieve the state-of-the-art performance among various Collaborative Filtering (CF) approaches for recommendation. Despite the high recommendation accuracy of LFMs…
Collaborative FilteringMV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation
Sequential recommendation is a fundamental task for network applications, and it usually suffers from the item cold start problem due to the insufficiency of user feedbacks. There are currently three kinds of popular app…
Collaborative FilteringSequential RecommendationDeep Item-based Collaborative Filtering for Top-N Recommendation
Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's p…
Collaborative FilteringDecision MakingRecommendation SystemsSocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation
Collaborative Filtering (CF) is one of the most successful approaches for recommender systems. With the emergence of online social networks, social recommendation has become a popular research direction. Most of these so…
Collaborative FilteringRecommendation SystemsCollaborative Filtering with Stability
Collaborative filtering (CF) is a popular technique in today's recommender systems, and matrix approximation-based CF methods have achieved great success in both rating prediction and top-N recommendation tasks. However,…
Collaborative FilteringRecommendation SystemsGEMRank: Global Entity Embedding For Collaborative Filtering
Recently, word embedding algorithms have been applied to map the entities of recommender systems, such as users and items, to new feature spaces using textual element-context relations among them. Unlike many other domai…
Collaborative FilteringRecommendation SystemsLeveraging Virtual and Real Person for Unsupervised Person Re-identification
Person re-identification (re-ID) is a challenging problem especially when no labels are available for training. Although recent deep re-ID methods have achieved great improvement, it is still difficult to optimize deep r…
Collaborative FilteringPerson Re-IdentificationStyle TransferUnsupervised Person Re-IdentificationA latent topic model for mining heterogenous non-randomly missing electronic health records data
Electronic health records (EHR) are rich heterogeneous collection of patient health information, whose broad adoption provides great opportunities for systematic health data mining. However, heterogeneous EHR data types …
Collaborative FilteringTopic ModelsFrom Gene Expression to Drug Response: A Collaborative Filtering Approach
Predicting the response of cancer cells to drugs is an important problem in pharmacogenomics. Recent efforts in generation of large scale datasets profiling gene expression and drug sensitivity in cell lines have provide…
Collaborative FilteringSensitivityDeep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
Recommendation is crucial in both academia and industry, and various techniques are proposed such as content-based collaborative filtering, matrix factorization, logistic regression, factorization machines, neural networ…
Collaborative FilteringDecision MakingDeep Reinforcement LearningMulti-Armed Bandits+5Alternating Linear Bandits for Online Matrix-Factorization Recommendation
We consider the problem of online collaborative filtering in the online setting, where items are recommended to the users over time. At each time step, the user (selected by the environment) consumes an item (selected by…
Collaborative FilteringAttribute-aware Collaborative Filtering: Survey and Classification
Attribute-aware CF models aims at rating prediction given not only the historical rating from users to items, but also the information associated with users (e.g. age), items (e.g. price), or even ratings (e.g. rating ti…
AttributeClassificationCollaborative FilteringGeneral Classification+1Neural Variational Hybrid Collaborative Filtering
Collaborative Filtering (CF) is one of the most used methods for Recommender System. Because of the Bayesian nature and nonlinearity, deep generative models, e.g. Variational Autoencoder (VAE), have been applied into CF …
Collaborative FilteringRecommendation SystemsMusic Sequence Prediction with Mixture Hidden Markov Models
Recommendation systems that automatically generate personalized music playlists for users have attracted tremendous attention in recent years. Nowadays, most music recommendation systems rely on item-based or user-based …
Collaborative FilteringMusic RecommendationPredictionRecommendation SystemsVECTORIZATION METHODS IN RECOMMENDER SYSTEM
The most used recommendation method is collaborative filtering, and the key part of collaborative filtering is to compute the similarity. The similarity based on co-occurrence of similar event is easy to implement and ca…
Collaborative FilteringPositionRecommendation SystemsPredicting computational reproducibility of data analysis pipelines in large population studies using collaborative filtering
Evaluating the computational reproducibility of data analysis pipelines has become a critical issue. It is, however, a cumbersome process for analyses that involve data from large populations of subjects, due to their co…
Collaborative FilteringLearning Consumer and Producer Embeddings for User-Generated Content Recommendation
User-Generated Content (UGC) is at the core of web applications where users can both produce and consume content. This differs from traditional e-Commerce domains where content producers and consumers are usually from tw…
Collaborative FilteringVariational Collaborative Learning for User Probabilistic Representation
Collaborative filtering (CF) has been successfully employed by many modern recommender systems. Conventional CF-based methods use the user-item interaction data as the sole information source to recommend items to users.…
Collaborative FilteringRecommendation SystemsNAIS: Neural Attentive Item Similarity Model for Recommendation
Item-to-item collaborative filtering (aka. item-based CF) has been long used for building recommender systems in industrial settings, owing to its interpretability and efficiency in real-time personalization. It builds a…
Collaborative FilteringRecommendation Systemscf2vec: Collaborative Filtering algorithm selection using graph distributed representations
Algorithm selection using Metalearning aims to find mappings between problem characteristics (i.e. metafeatures) with relative algorithm performance to predict the best algorithm(s) for new datasets. Therefore, it is of …
Collaborative FilteringRepresentation Learning