Papers Collaborative Filtering
“Collaborative Filtering” 태그가 달린 논문 1,408편 · 필터 해제
Thematic recommendations on knowledge graphs using multilayer networks
We present a framework to generate and evaluate thematic recommendations based on multilayer network representations of knowledge graphs (KGs). In this representation, each layer encodes a different type of relationship …
Collaborative FilteringKnowledge GraphsMovie RecommendationCo-Factorization Model for Collaborative Filtering with Session-based Data
Matrix factorization (MF) is a common method for collaborative filtering. MF represents user preferences and item attributes by latent factors. Despite that MF is a powerful method, it suffers from not be able to identif…
Collaborative FilteringCounterfactual Explanations for Neural Recommenders
Understanding why specific items are recommended to users can significantly increase their trust and satisfaction in the system. While neural recommenders have become the state-of-the-art in recent years, the complexity …
Collaborative FilteringcounterfactualNeural Graph Matching based Collaborative Filtering
User and item attributes are essential side-information; their interactions (i.e., their co-occurrence in the sample data) can significantly enhance prediction accuracy in various recommender systems. We identify two dif…
AttributeCollaborative FilteringGraph LearningGraph Matching+1UGRec: Modeling Directed and Undirected Relations for Recommendation
Recommender systems, which merely leverage user-item interactions for user preference prediction (such as the collaborative filtering-based ones), often face dramatic performance degradation when the interactions of user…
AttributeCollaborative FilteringRecommendation SystemsRelationClick-Through Rate Prediction Using Graph Neural Networks and Online Learning
Recommendation systems have been extensively studied by many literature in the past and are ubiquitous in online advertisement, shopping industry/e-commerce, query suggestions in search engines, and friend recommendation…
Binary ClassificationClick-Through Rate PredictionCollaborative FilteringRecommendation SystemsStronger Privacy for Federated Collaborative Filtering with Implicit Feedback
Recommender systems are commonly trained on centrally collected user interaction data like views or clicks. This practice however raises serious privacy concerns regarding the recommender's collection and handling of pot…
Collaborative FilteringRecommendation SystemsAlgorithms are not neutral: Bias in collaborative filtering
Discussions of algorithmic bias tend to focus on examples where either the data or the people building the algorithms are biased. This gives the impression that clean data and good intentions could eliminate bias. The ne…
Collaborative FilteringDecision MakingSelection biasLaboRecommender: A crazy-easy to use Python-based recommender system for laboratory tests
Laboratory tests play a major role in clinical decision making because they are essential for the confirmation of diagnostics suspicions and influence medical decisions. The number of different laboratory tests available…
Collaborative FilteringDecision MakingRecommendation SystemsBI-REC: Guided Data Analysis for Conversational Business Intelligence
Conversational interfaces to Business Intelligence (BI) applications enable data analysis using a natural language dialog in small incremental steps. To truly unleash the power of conversational BI to democratize access …
Collaborative FilteringConversational RecommendationA Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation
Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we hav…
Collaborative FilteringRecommendation SystemsSequential RecommendationRepresent Items by Items: An Enhanced Representation of the Target Item for Recommendation
Item-based collaborative filtering (ICF) has been widely used in industrial applications such as recommender system and online advertising. It models users' preference on target items by the items they have interacted wi…
Collaborative FilteringRecommendation SystemsRecommending Burgers based on Pizza Preferences: Addressing Data Sparsity with a Product of Experts
In this paper, we describe a method to tackle data sparsity and create recommendations in domains with limited knowledge about user preferences. We expand the variational autoencoder collaborative filtering from a single…
Collaborative FilteringAttention on Global-Local Representation Spaces in Recommender Systems
In this study, we present a novel clustering-based collaborative filtering (CF) method for recommender systems. Clustering-based CF methods can effectively deal with data sparsity and scalability problems. However, most …
ClusteringCollaborative FilteringRecommendation SystemsMining Latent Structures for Multimedia Recommendation
Multimedia content is of predominance in the modern Web era. Investigating how users interact with multimodal items is a continuing concern within the rapid development of recommender systems. The majority of previous wo…
Collaborative FilteringMultimedia recommendationMulti-modal RecommendationMultimodal Recommendation+1NeuSE: A Neural Snapshot Ensemble Method for Collaborative Filtering
In collaborative filtering (CF) algorithms, the optimal models are usually learned by globally minimizing the empirical risks averaged over all the observed data. However, the global models are often obtained via a perfo…
Collaborative FilteringEnsemble LearningHyperbolic Neural Collaborative Recommender
This paper explores the use of hyperbolic geometry and deep learning techniques for recommendation. We present Hyperbolic Neural Collaborative Recommender (HNCR), a deep hyperbolic representation learning method that exp…
Collaborative FilteringRepresentation LearningWhen and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution
Collaborative bandit learning, i.e., bandit algorithms that utilize collaborative filtering techniques to improve sample efficiency in online interactive recommendation, has attracted much research attention as it enjoys…
Bayesian InferenceCollaborative FilteringInteractive RecommendationModel Selection+2Exploring Implicit Sentiment Evoked by Fine-grained News Events
We investigate the feasibility of defining sentiment evoked by fine-grained news events. Our research question is based on the premise that methods for detecting implicit sentiment in news can be a key driver of content …
ArticlesCollaborative FilteringDiversitySentiment AnalysisProjected Hamming Dissimilarity for Bit-Level Importance Coding in Collaborative Filtering
When reasoning about tasks that involve large amounts of data, a common approach is to represent data items as objects in the Hamming space where operations can be done efficiently and effectively. Object similarity can …
Collaborative Filtering