Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems
In recent years, the emerging topics of recommender systems that take advantage of natural language processing techniques have attracted much attention, and one of their applications is the Conversational Recommender Sys…
Collaborative FilteringDeep LearningGoal-Oriented Dialogue SystemsRecommendation SystemsPrescribing Deep Attentive Score Prediction Attracts Improved Student Engagement
Intelligent Tutoring Systems (ITSs) have been developed to provide students with personalized learning experiences by adaptively generating learning paths optimized for each individual. Within the vast scope of ITS, scor…
Collaborative FilteringDiagnosticPredictionNeural Network-Based Collaborative Filtering for Question Sequencing
E-Learning systems (ELS) and Intelligent Tutoring Systems (ITS) play a significant part in today's education programs. Sequencing questions is the art of generating a personalized quiz for a target learner. A personalize…
Collaborative FilteringA session-based song recommendation approach involving user characterization along the play power-law distribution
In recent years, streaming music platforms have become very popular mainly due to the huge number of songs these systems make available to users. This enormous availability means that recommendation mechanisms that help …
Collaborative FilteringRecommendation SystemsGraph Learning Approaches to Recommender Systems: A Review
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS mainly employ the advanced graph learning approaches to model users' preferences and intenti…
Collaborative FilteringGraph LearningRecommendation SystemsConditioned Variational Autoencoder for top-N item recommendation
In this paper, we propose a Conditioned Variational Autoencoder (C-VAE) for constrained top-N item recommendation where the recommended items must satisfy a given condition. The proposed model architecture is similar to …
Collaborative FilteringLarge-scale Real-time Personalized Similar Product Recommendations
Similar product recommendation is one of the most common scenes in e-commerce. Many recommendation algorithms such as item-to-item Collaborative Filtering are working on measuring item similarities. In this paper, we int…
Collaborative FilteringProduct RecommendationReliable Time Prediction in the Markov Stochastic Block Model
We introduce the Markov Stochastic Block Model (MSBM): a growth model for community based networks where node attributes are assigned through a Markovian dynamic. We rely on HMMs' literature to design prediction methods …
ClusteringCollaborative FilteringCommunity DetectionLink Prediction+3CSRN: Collaborative Sequential Recommendation Networks for News Retrieval
Nowadays, news apps have taken over the popularity of paper-based media, providing a great opportunity for personalization. Recurrent Neural Network (RNN)-based sequential recommendation is a popular approach that utiliz…
ArticlesCollaborative FilteringNews RetrievalRetrieval+1Deep Match to Rank Model for Personalized Click-Through Rate Prediction
Click-through rate (CTR) prediction is a core task in the field of recommender system and many other applications. For CTR prediction model, personalization is the key to improve the performance and enhance the user expe…
Click-Through Rate PredictionCollaborative FilteringPredictionRecommendation SystemsExtending a Tag-based Collaborative Recommender with Co-occurring Information Interests
Collaborative Filtering is largely applied to personalize item recommendation but its performance is affected by the sparsity of rating data. In order to address this issue, recent systems have been developed to improve …
Collaborative FilteringDiversityTAGEmpirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering
In this work, we study the utility of graph embeddings to generate latent user representations for trust-based collaborative filtering. In a cold-start setting, on three publicly available datasets, we evaluate approache…
Collaborative FilteringNetwork EmbeddingNonconvex Matrix Completion with Linearly Parameterized Factors
Techniques of matrix completion aim to impute a large portion of missing entries in a data matrix through a small portion of observed ones. In practice including collaborative filtering, prior information and special str…
Collaborative FilteringMatrix CompletionMulti-faceted Trust-based Collaborative Filtering
Many collaborative recommender systems leverage social correlation theories to improve suggestion performance. However, they focus on explicit relations between users and they leave out other types of information that ca…
Collaborative FilteringRecommendation SystemsMulti-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation
Hashing is an effective technique to address the large-scale recommendation problem, due to its high computation and storage efficiency on calculating the user preferences on items. However, existing hashing-based recomm…
Collaborative FilteringQuantizationSurvey of Privacy-Preserving Collaborative Filtering
Collaborative filtering recommendation systems provide recommendations to users based on their own past preferences, as well as those of other users who share similar interests. The use of recommendation systems has grow…
Collaborative FilteringPrivacy PreservingRecommendation SystemsSurveyUncovering the Data-Related Limits of Human Reasoning Research: An Analysis based on Recommender Systems
Understanding the fundamentals of human reasoning is central to the development of any system built to closely interact with humans. Cognitive science pursues the goal of modeling human-like intelligence from a theory-dr…
Collaborative FilteringRecommendation SystemsPrice-aware Recommendation with Graph Convolutional Networks
In recent years, much research effort on recommendation has been devoted to mining user behaviors, i.e., collaborative filtering, along with the general information which describes users or items, e.g., textual attribute…
Collaborative FilteringMarketingRecommendation SystemsAdvances in Collaborative Filtering and Ranking
In this dissertation, we cover some recent advances in collaborative filtering and ranking. In chapter 1, we give a brief introduction of the history and the current landscape of collaborative filtering and ranking; chap…
Collaborative FilteringCollaborative RankingSequential RecommendationCATA++: A Collaborative Dual Attentive Autoencoder Method for Recommending Scientific Articles
Recommender systems today have become an essential component of any commercial website. Collaborative filtering approaches, and Matrix Factorization (MF) techniques in particular, are widely used in recommender systems. …
ArticlesCollaborative FilteringRecommendation Systems