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Neighbor Selection and Weighting in User-Based Collaborative Filtering
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What this work says
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A Comprehensive Survey of Neighborhood-based Recommendation Methods
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A Comprehensive Survey of Neighborhood-Based Recommendation Methods
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Using Trust in Recommender Systems: An Experimental Analysis
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Analyzing Correlation between Trust and User Similarity in Online Communities
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Using Trust in Collaborative Filtering Recommendation
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Multidimensional credibility model for neighbor selection in collaborative recommendation
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An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms
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Significance Testing of the Spearman Rank Correlation Coefficient
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A collaborative filtering algorithm and evaluation metric that accurately model the user experience
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Learning to estimate query difficulty
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Improving collaborative filtering with trust-based metrics
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Effective missing data prediction for collaborative filtering
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Learning to recommend with social trust ensemble
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Personalised and dynamic trust in social networks
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Ensemble methods for improving the performance of neighborhood-based collaborative filtering
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Increasing consumers' understanding of recommender results
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Comparison of collaborative filtering algorithms
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The efficient imputation method for neighborhood-based collaborative filtering
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A Survey of Collaborative Filtering Techniques
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Estimating the Query Difficulty for Information Retrieval
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Significance Testing of the Spearman Rank Correlation Coefficient
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Adaptive active learning in recommender systems
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Improving memory-based collaborative filtering by neighbour selection based on user preference overlap
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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