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Showing 141 - 160 results of 52,286 for search '(( significant increase decrease ) OR ( significant user preference ))', query time: 0.77s Refine Results
  1. 141
    Results of significance for regression factors.

    Results of significance for regression factors. by Junxia Zhou (14381598)

    Published 2024
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  2. 142
    Variation in Item Degree Diversity (IDD) for different algorithms and neighbor selections.

    Variation in Item Degree Diversity (IDD) for different algorithms and neighbor selections. by Zhan Su (264040)

    Published 2023
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  3. 143
    HLU results of different algorithms on various datasets.

    HLU results of different algorithms on various datasets. by Zhan Su (264040)

    Published 2023
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  4. 144
    For relevant and irrelevant items, classify items into four categories based on whether the system makes a recommendation.

    For relevant and irrelevant items, classify items into four categories based on whether the system makes a recommendation. by Zhan Su (264040)

    Published 2023
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  5. 145
    Label vectors of items.

    Label vectors of items. by Zhan Su (264040)

    Published 2023
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  6. 146
    Flowchart of the algorithm.

    Flowchart of the algorithm. by Zhan Su (264040)

    Published 2023
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  7. 147
    Comparison of RMSE results for different algorithms with varying numbers of neighbors.

    Comparison of RMSE results for different algorithms with varying numbers of neighbors. by Zhan Su (264040)

    Published 2023
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  8. 148
    Comparison of MAE results for different algorithms with varying numbers of neighbors.

    Comparison of MAE results for different algorithms with varying numbers of neighbors. by Zhan Su (264040)

    Published 2023
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  9. 149
    SA values of different algorithms when selecting different numbers of neighbors.

    SA values of different algorithms when selecting different numbers of neighbors. by Zhan Su (264040)

    Published 2023
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  10. 150
    Comparison of prediction time for different algorithms on MovieLens-25M and Netflix datasets (unit: Minutes).

    Comparison of prediction time for different algorithms on MovieLens-25M and Netflix datasets (unit: Minutes). by Zhan Su (264040)

    Published 2023
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  11. 151
    Comparison of F1 scores for different algorithms with varying numbers of neighbors.

    Comparison of F1 scores for different algorithms with varying numbers of neighbors. by Zhan Su (264040)

    Published 2023
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  12. 152
    Ranking of genotypes according to the ideotype for each trait and the percentage difference (increase or decrease) in relation to cv. M-82.

    Ranking of genotypes according to the ideotype for each trait and the percentage difference (increase or decrease) in relation to cv. M-82. by Herika Paula Pessoa (13254326)

    Published 2023
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  13. 153
    Subjective evaluation procedure.

    Subjective evaluation procedure. by Seonghyeon Kim (4536112)

    Published 2024
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  14. 154
    Fig 5 -

    Fig 5 - by Seonghyeon Kim (4536112)

    Published 2024
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  15. 155
    Representation of typical tip-in acceleration profiles.

    Representation of typical tip-in acceleration profiles. by Seonghyeon Kim (4536112)

    Published 2024
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  16. 156
    Dynamic characteristics of each acceleration profile in Experiment 3.

    Dynamic characteristics of each acceleration profile in Experiment 3. by Seonghyeon Kim (4536112)

    Published 2024
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  17. 157
    Bradley-Terry model estimates for Experiment 2.

    Bradley-Terry model estimates for Experiment 2. by Seonghyeon Kim (4536112)

    Published 2024
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  18. 158
    Win matrix for acceleration profiles in Experiment 3.

    Win matrix for acceleration profiles in Experiment 3. by Seonghyeon Kim (4536112)

    Published 2024
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  19. 159
    Experiment data.

    Experiment data. by Seonghyeon Kim (4536112)

    Published 2024
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  20. 160
    Win matrix for acceleration profiles in Experiment 1.

    Win matrix for acceleration profiles in Experiment 1. by Seonghyeon Kim (4536112)

    Published 2024
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