Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation
Heterogeneous information network has been widely used to alleviate sparsity and cold start problems in recommender systems since it can model rich context information in user-item interactions. Graph neural network is a…
Collaborative FilteringGraph AttentionGraph Neural NetworkRecommendation SystemsA Comprehensive Review on Non-Neural Networks Collaborative Filtering Recommendation Systems
Over the past two decades, recommender systems have attracted a lot of interest due to the explosion in the amount of data in online applications. A particular attention has been paid to collaborative filtering, which is…
BIG-bench Machine LearningCollaborative FilteringRecommendation SystemsContext-Aware Legal Citation Recommendation using Deep Learning
Lawyers and judges spend a large amount of time researching the proper legal authority to cite while drafting decisions. In this paper, we develop a citation recommendation tool that can help improve efficiency in the pr…
Citation RecommendationCollaborative FilteringDeep Learningtext similarityEfficient Data-specific Model Search for Collaborative Filtering
Collaborative filtering (CF), as a fundamental approach for recommender systems, is usually built on the latent factor model with learnable parameters to predict users' preferences towards items. However, designing a pro…
AutoMLCollaborative FilteringmodelRecommendation SystemsMulti-output Gaussian Processes for Uncertainty-aware Recommender Systems
Recommender systems are often designed based on a collaborative filtering approach, where user preferences are predicted by modelling interactions between users and items. Many common approaches to solve the collaborativ…
Collaborative FilteringGaussian ProcessesRecommendation SystemsRepresentation LearningA novel method for recommendation systems using invasive weed optimization
One of the popular approaches in recommendation systems is Collaborative Filtering (CF). The most significant step in CF is choosing the appropriate set of users. For this purpose, similarity measures are usually used fo…
Collaborative FilteringRecommendation SystemsFederated Neural Collaborative Filtering
In this work, we present a federated version of the state-of-the-art Neural Collaborative Filtering (NCF) approach for item recommendations. The system, named FedNCF, enables learning without requiring users to disclose …
Collaborative FilteringFederated LearningPrivacy PreservingRecommendation SystemsGraph-based Exercise- and Knowledge-Aware Learning Network for Student Performance Prediction
Predicting student performance is a fundamental task in Intelligent Tutoring Systems (ITSs), by which we can learn about students' knowledge level and provide personalized teaching strategies for them. Researchers have m…
Collaborative FilteringYou Sound Like Someone Who Watches Drama Movies: Towards Predicting Movie Preferences from Conversational Interactions
The increasing popularity of voice-based personal assistants provides new opportunities for conversational recommendation. One particularly interesting area is movie recommendation, which can benefit from an open-ended i…
Collaborative FilteringConversational RecommendationDomain AdaptationMovie RecommendationPrivileged Graph Distillation for Cold Start Recommendation
The cold start problem in recommender systems is a long-standing challenge, which requires recommending to new users (items) based on attributes without any historical interaction records. In these recommendation systems…
AttributeCollaborative FilteringRecommendation SystemsCausCF: Causal Collaborative Filtering for RecommendationEffect Estimation
To improve user experience and profits of corporations, modern industrial recommender systems usually aim to select the items that are most likely to be interacted with (e.g., clicks and purchases). However, they overloo…
Collaborative FilteringRecommendation SystemsA Hybrid Recommender System for Recommending Smartphones to Prospective Customers
Recommender Systems are a subclass of machine learning systems that employ sophisticated information filtering strategies to reduce the search time and suggest the most relevant items to any particular user. Hybrid recom…
Collaborative FilteringRecommendation SystemsHybrid Movie Recommender System based on Resource Allocation
Recommender Systems are inevitable to personalize user's experiences on the Internet. They are using different approaches to recommend the Top-K items to users according to their preferences. Nowadays recommender systems…
ClusteringCollaborative FilteringRecommendation SystemsCriterion-based Heterogeneous Collaborative Filtering for Multi-behavior Implicit Recommendation
Recent years have witnessed the explosive growth of interaction behaviors in multimedia information systems, where multi-behavior recommender systems have received increasing attention by leveraging data from various aux…
Collaborative FilteringMetric LearningRecommendation SystemsA Load Balanced Recommendation Approach
Recommender systems (RSs) are software tools and algorithms developed to alleviate the problem of information overload, which makes it difficult for a user to make right decisions. Two main paradigms toward the recommend…
Collaborative FilteringDiversityRecommendation SystemsSequential Automated Machine Learning: Bandits-driven Exploration using a Collaborative Filtering Representation
The goal of Automated Machine Learning (AutoML) is to make Machine Learning (ML) tools more accessible. Collaborative Filtering (CF) methods have shown great success in automating the creation of machine learning pipelin…
AutoMLBIG-bench Machine LearningCollaborative FilteringSet2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation
As users often express their preferences with binary behavior data~(implicit feedback), such as clicking items or buying products, implicit feedback based Collaborative Filtering~(CF) models predict the top ranked items …
Collaborative FilteringGraph Learning based Recommender Systems: A Review
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS employ advanced graph learning approaches to model users' preferences and intentions as well…
Collaborative FilteringGraph LearningRecommendation SystemsBootstrapping User and Item Representations for One-Class Collaborative Filtering
The goal of one-class collaborative filtering (OCCF) is to identify the user-item pairs that are positively-related but have not been interacted yet, where only a small portion of positive user-item interactions (e.g., u…
Collaborative FilteringData AugmentationCausal Intervention for Leveraging Popularity Bias in Recommendation
Recommender system usually faces popularity bias issues: from the data perspective, items exhibit uneven (long-tail) distribution on the interaction frequency; from the method perspective, collaborative filtering methods…
Collaborative FilteringRecommendation Systems