Papers Collaborative Filtering
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Data-driven model for hydraulic fracturing design optimization: focus on building digital database and production forecast
Growing amount of hydraulic fracturing (HF) jobs in the recent two decades resulted in a significant amount of measured data available for development of predictive models via machine learning (ML). In multistage fractur…
Collaborative FilteringImputationMissing ValuesUSTAR: Online Multimodal Embedding for Modeling User-Guided Spatiotemporal Activity
Building spatiotemporal activity models for people's activities in urban spaces is important for understanding the ever-increasing complexity of urban dynamics. With the emergence of Geo-Tagged Social Media (GTSM) record…
Collaborative FilteringEvent DetectionRetrievalMeta Matrix Factorization for Federated Rating Predictions
Federated recommender systems have distinct advantages in terms of privacy protection over traditional recommender systems that are centralized at a data center. However, previous work on federated recommender systems do…
Collaborative FilteringFederated LearningRecommendation SystemsMarkov Random Fields for Collaborative Filtering
In this paper, we model the dependencies among the items that are recommended to a user in a collaborative-filtering problem via a Gaussian Markov Random Field (MRF). We build upon Besag's auto-normal parameterization an…
Collaborative FilteringLearning to Recommend from Sparse Data via Generative User Feedback
Traditional collaborative filtering (CF) based recommender systems tend to perform poorly when the user-item interactions/ratings are highly scarce. To address this, we propose a learning framework that improves collabor…
Collaborative FilteringRecommendation SystemsReinforcement LearningCollaborative Preference Embedding against Sparse Labels
Living in the era of the internet, we are now facing a big bang of online information. As a consequence, we often find ourselves troubling with hundreds and thousands of options before making a decision. As a way to impr…
Collaborative FilteringDecision MakingMetric LearningRecommendation Systems+1Collaborative Filtering with Label Consistent Restricted Boltzmann Machine
The possibility of employing restricted Boltzmann machine (RBM) for collaborative filtering has been known for about a decade. However, there has been hardly any work on this topic since 2007. This work revisits the appl…
Collaborative FilteringRecommendation SystemsNeural Logic Networks
Recent years have witnessed the great success of deep neural networks in many research areas. The fundamental idea behind the design of most neural networks is to learn similarity patterns from data for prediction and in…
Collaborative FilteringLogical ReasoningAn Intelligent Data Analysis for Hotel Recommendation Systems using Machine Learning
This paper presents an intelligent approach to handle heterogeneous and large-sized data using machine learning to generate true recommendations for the future customers. The Collaborative Filtering (CF) approach is one …
BIG-bench Machine LearningCollaborative FilteringLexical AnalysisRecommendation Systems+1Parallelized Training of Restricted Boltzmann Machines using Markov-Chain Monte Carlo Methods
Restricted Boltzmann Machine (RBM) is a generative stochastic neural network that can be applied to collaborative filtering technique used by recommendation systems. Prediction accuracy of the RBM model is usually better…
Collaborative FilteringCPURecommendation SystemsPrivacy-preserving and yet Robust Collaborative Filtering Recommender as a Service
Collaborative filtering recommenders provide effective personalization services at the cost of sacrificing the privacy of their end users. Due to the increasing concerns from the society and stricter privacy regulations,…
Collaborative FilteringPrivacy PreservingBeyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs
Learning useful representations is a key ingredient to the success of modern machine learning. Currently, representation learning mostly relies on embedding data into Euclidean space. However, recent work has shown that …
Collaborative FilteringInductive BiasRepresentation LearningDBRec: Dual-Bridging Recommendation via Discovering Latent Groups
In recommender systems, the user-item interaction data is usually sparse and not sufficient for learning comprehensive user/item representations for recommendation. To address this problem, we propose a novel dual-bridgi…
Collaborative FilteringRecommendation SystemsFNHSM_HRS: Hybrid recommender system using fuzzy clustering and heuristic similarity measure
Nowadays, Recommender Systems have become a comprehensive system for helping and guiding users in a huge amount of data on the Internet. Collaborative Filtering offers to active users based on the rating of a set of user…
ClusteringCollaborative FilteringRecommendation SystemsMixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems
Embedding representations power machine intelligence in many applications, including recommendation systems, but they are space intensive -- potentially occupying hundreds of gigabytes in large-scale settings. To help ma…
Click-Through Rate PredictionCollaborative FilteringRecommendation SystemsVariational Hashing-based Collaborative Filtering with Self-Masking
Hashing-based collaborative filtering learns binary vector representations (hash codes) of users and items, such that recommendations can be computed very efficiently using the Hamming distance, which is simply the sum o…
Collaborative FilteringQuantitative analysis of Matthew effect and sparsity problem of recommender systems
Recommender systems have received great commercial success. Recommendation has been used widely in areas such as e-commerce, online music FM, online news portal, etc. However, several problems related to input data struc…
Collaborative FilteringRecommendation SystemsNatural Language Processing via LDA Topic Model in Recommendation Systems
Today, Internet is one of the widest available media worldwide. Recommendation systems are increasingly being used in various applications such as movie recommendation, mobile recommendation, article recommendation and e…
ArticlesCollaborative FilteringMovie RecommendationRecommendation SystemsMovie Recommender Systems: Implementation and Performance Evaluation
Over the years, explosive growth in the number of items in the catalog of e-commerce businesses, such as Amazon, Netflix, Pandora, etc., have warranted the development of recommender systems to guide consumers towards th…
Collaborative FilteringRecommendation SystemsCross-domain recommender system using Generalized Canonical Correlation Analysis
Recommender systems provide personalized recommendations to the users from a large number of possible options in online stores. Matrix factorization is a well-known and accurate collaborative filtering approach for recom…
Collaborative FilteringRecommendation Systems