„Cold-Start Problem“
Suchergebnisse
1.000+ Treffer
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Learner’s intention analysis to mitigate the cold start problem in personalized learning recommendation systems
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Active learning algorithm for alleviating the user cold start problem of recommender systems
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An explanatory item response theory method for alleviating the cold-start problem in adaptive learning environments
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BERTopic: a model to solve cold start problem in software bug recommendation system
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Correction to: Learner’s intention analysis to mitigate the cold start problem in personalized learning recommendation systems
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The cold-start problem in nascent AI strategy: Kickstarting data network effects
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Mitigating cold start problem in serverless computing using predictive pre-warming with machine learning
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DemogCF model of personalized recommendations based on demographic characteristics for overcoming data sparsity and cold start problems
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Spatio-Temporal Recommendation in Social Media
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An autoencoder-based recommendation framework toward cold start problem
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A novel approach to movie recommendation systems: overcoming the cold start problem with dynamic sea-horse slimmable network with a contextual attention network
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On the cold start problem in transient simulations with coupled atmosphere-ocean models
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Trend Analysis with Interpretability and Cold-Start Problems for Recommender Systems
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A systematic literature review of solutions for cold start problem
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On the cold start problem in transient simulations with coupled atmosphere-ocean models
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Employing neighborhood reduction for alleviating sparsity and cold start problems in user-based collaborative filtering
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A deep learning approach for context-aware citation recommendation using rhetorical zone classification and similarity to overcome cold-start problem
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Approaches and algorithms to mitigate cold start problems in recommender systems: a systematic literature review
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Addressing the cold-start problem of recommendation systems for financial products by using few-shot deep learning
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Incorporating multidimensional information into dynamic recommendation process to cope with cold start and data sparsity problems