Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
LCMR: Local and Centralized Memories for Collaborative Filtering with Unstructured Text
Collaborative filtering (CF) is the key technique for recommender systems. Pure CF approaches exploit the user-item interaction data (e.g., clicks, likes, and views) only and suffer from the sparsity issue. Items are usu…
ArticlesCollaborative FilteringRecommendation SystemsDistributed Collaborative Hashing and Its Applications in Ant Financial
Collaborative filtering, especially latent factor model, has been popularly used in personalized recommendation. Latent factor model aims to learn user and item latent factors from user-item historic behaviors. To apply …
Collaborative FilteringCollaborative Filtering with Topic and Social Latent Factors Incorporating Implicit Feedback
Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized items for different users. Latent factors based collaborative filtering (CF) has become the pop…
Collaborative FilteringRecommendation SystemsA trust-based recommendation method using network diffusion processes
A variety of rating-based recommendation methods have been extensively studied including the well-known collaborative filtering approaches and some network diffusion-based methods, however, social trust relations are not…
Collaborative FilteringDiversityLearning over Knowledge-Base Embeddings for Recommendation
State-of-the-art recommendation algorithms -- especially the collaborative filtering (CF) based approaches with shallow or deep models -- usually work with various unstructured information sources for recommendation, suc…
Collaborative FilteringRecommendation SystemsRepresentation LearningRippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance. This paper c…
Click-Through Rate PredictionCollaborative FilteringNews RecommendationRecommendation SystemsCross-domain Recommendation via Deep Domain Adaptation
The behavior of users in certain services could be a clue that can be used to infer their preferences and may be used to make recommendations for other services they have never used. However, the cross-domain relationshi…
Collaborative FilteringDenoisingDomain AdaptationMulti-class ClassificationDeep Models of Interactions Across Sets
We use deep learning to model interactions across two or more sets of objects, such as user-movie ratings, protein-drug bindings, or ternary user-item-tag interactions. The canonical representation of such interactions i…
Collaborative FilteringMatrix CompletionRecommendation SystemsTAGStochastic Block Models with Multiple Continuous Attributes
The stochastic block model (SBM) is a probabilistic model for community structure in networks. Typically, only the adjacency matrix is used to perform SBM parameter inference. In this paper, we consider circumstances in …
AttributeCollaborative FilteringCommunity DetectionLink Prediction+1Collaborative Metric Learning Recommendation System: Application to Theatrical Movie Releases
Product recommendation systems are important for major movie studios during the movie greenlight process and as part of machine learning personalization pipelines. Collaborative Filtering (CF) models have proved to be ef…
Collaborative FilteringMetric LearningProduct RecommendationRecommendation SystemsHarnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation
Low-rank modeling plays a pivotal role in signal processing and machine learning, with applications ranging from collaborative filtering, video surveillance, medical imaging, to dimensionality reduction and adaptive filt…
Collaborative FilteringDimensionality ReductionHybridSVD: When Collaborative Information is Not Enough
We propose a new hybrid algorithm that allows incorporating both user and item side information within the standard collaborative filtering technique. One of its key features is that it naturally extends a simple PureSVD…
Collaborative FilteringModel SelectionVariational Autoencoders for Collaborative Filtering
We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback. This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still la…
Bayesian InferenceCollaborative FilteringLanguage ModelingLanguage Modelling+2Variational Autoencoders for Collaborative Filtering
We extend variational autoencoders (vaes) to collaborative filtering for implicit feedback. This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still la…
Bayesian InferenceCollaborative FilteringLanguage ModelingLanguage Modelling+2Necessary and Sufficient Null Space Condition for Nuclear Norm Minimization in Low-Rank Matrix Recovery
Low-rank matrix recovery has found many applications in science and engineering such as machine learning, signal processing, collaborative filtering, system identification, and Euclidean embedding. But the low-rank matri…
Collaborative FilteringNeural Tensor Factorization
Neural collaborative filtering (NCF) and recurrent recommender systems (RRN) have been successful in modeling user-item relational data. However, they are also limited in their assumption of static or sequential modeling…
Collaborative FilteringLink PredictionRecommendation SystemsSide Information Fusion for Recommender Systems over Heterogeneous Information Network
Collaborative filtering (CF) has been one of the most important and popular recommendation methods, which aims at predicting users' preferences (ratings) based on their past behaviors. Recently, various types of side inf…
Collaborative FilteringRecommendation SystemsCityTransfer: Transferring Inter- and Intra-City Knowledge for Chain Store Site Recommendation based on Multi-Source Urban Data
Chain businesses have been dominating the market in many parts of the world. It is important to identify the optimal locations for a new chain store. Recently, numerous studies have been done on chain store location reco…
Collaborative FilteringTransfer LearningIndian Regional Movie Dataset for Recommender Systems
Indian regional movie dataset is the first database of regional Indian movies, users and their ratings. It consists of movies belonging to 18 different Indian regional languages and metadata of users with varying demogra…
Collaborative Filteringcompressed sensingDiversityMatrix Completion+1Differentially Private Matrix Completion Revisited
We provide the first provably joint differentially private algorithm with formal utility guarantees for the problem of user-level privacy-preserving collaborative filtering. Our algorithm is based on the Frank-Wolfe meth…
Collaborative FilteringMatrix CompletionPrivacy Preserving