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Deep Learning Recommendations of E-Education Based on Clustering and Sequence
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Recommender Systems Handbook
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Methods and metrics for cold-start recommendations
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Item-based top- N recommendation algorithms
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A Survey of Collaborative Filtering Techniques
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Web crawling based context aware recommender system using optimized deep recurrent neural network
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TopicMF: Simultaneously Exploiting Ratings and Reviews for Recommendation
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DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
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Deep Embedded Fuzzy Clustering Model for Collaborative Filtering Recommender System
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Modeling Speech Emotion Recognition via Attention-Oriented Parallel CNN Encoders
inferredcites →https://doi.org/10.3390/electronics12010157openalex-snapshot
E-Learning Course Recommender System Using Collaborative Filtering Models
inferredcites →https://doi.org/10.3390/info12050198openalex-snapshot
Exploring Clustering-Based Reinforcement Learning for Personalized Book Recommendation in Digital Library
inferredcites →https://doi.org/10.3390/math9020197openalex-snapshot
Design and Analysis of a Cluster-Based Intelligent Hybrid Recommendation System for E-Learning Applications
inferredcites →https://doi.org/10.3390/pr9081454openalex-snapshot
Deep-Sequence–Aware Candidate Generation for e-Learning System
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A neural probabilistic language model
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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