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SCORING MEMBERS OF A SET DEPENDENT ON ELICITING PREFERENCE DATA AMONGST SUBSETS SELECTED ACCORDING TO A HEIGHT-BALANCED TREE
SCORING MEMBERS OF A SET DEPENDENT ON ELICITING PREFERENCE DATA AMONGST SUBSETS SELECTED ACCORDING TO A HEIGHT-BALANCED TREE
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机译:根据高度平衡树在选择的子集之间选择偏好数据的集合得分成员
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摘要
A software voting or prediction system iteratively solicits participant preferences between members of a set, with a binary tree built used to minimize the number of iterations required. As each member of the set is considered, it is pairwise-compared with select members represented by nodes already in the binary tree, with iterations beginning at a root node of the tree and continuing to a leaf node. The newly considered member is placed as a new leaf node, and the tree is height-rebalanced as appropriate. Red-black tree coloring and tree rotation rules are optionally used for this purpose. Yes/no preference tallies are kept for each member of the set throughout the tree-building process and are ultimately used for scoring. Height-rebalancing of the tree helps minimize the number of iterations needed to precisely score each member of the set relative to its alternatives.
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