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The Adaptive Clustering Method for the Long Tail Problem of Recommender Systems
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Instance-Based Learning Algorithms
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Induction of One-Level Decision Trees
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Learning and Revising User Profiles: The Identification of Interesting Web Sites
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E-Commerce Recommendation Applications
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Personalization of Supermarket Product Recommendations
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Expert-Driven Validation of Rule-Based User Models in Personalization Applications
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Efficient Adaptive-Support Association Rule Mining for Recommender Systems
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A tutorial on support vector regression
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Improving Accuracy of Recommender System by Item Clustering
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RBF and CBF neural network learning procedures
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Amazon.com recommendations: item-to-item collaborative filtering
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Least squares quantization in PCM
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Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
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The long tail of recommender systems and how to leverage it
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An algorithmic framework for performing collaborative filtering
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Content-based book recommending using learning for text categorization
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Item-based collaborative filtering recommendation algorithms
inferredcites →https://doi.org/10.1145/564376.564421openalex-snapshot
Methods and metrics for cold-start recommendations
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Fast Learning in Networks of Locally-Tuned Processing Units
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Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity
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Content-boosted collaborative filtering for improved recommendations
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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