【摘要】：為了提高元搜索引擎排序結(jié)果的質(zhì)量,提出了成員引擎特征的主題Hub值表示和基于主題Hub值的結(jié)果排序算法.特征學(xué)習(xí)算法利用一組主題關(guān)聯(lián)詞對成員引擎的特征進(jìn)行學(xué)習(xí),并表示為主題Hub值的形式.排序算法根據(jù)主題Hub值計(jì)算結(jié)果的全局相關(guān)度對結(jié)果進(jìn)行排序.實(shí)驗(yàn)結(jié)果表明,該模型取得了更好的排序質(zhì)量.
[Abstract]:In order to improve the quality of the sorting results of the meta search engine, the topic Hub value representation of the member engine features and the result sorting algorithm based on the topic Hub value are proposed. The feature learning algorithm uses a set of topic related words to learn the features of the member engine and expresses them as the Hub value of the topic. The sorting algorithm sorts the results according to the global correlation of the results calculated by the Hub value of the topic. Experimental results show that the model achieves better ranking quality.
【作者單位】： 北京工業(yè)大學(xué)計(jì)算機(jī)學(xué)院;
【分類號(hào)】：TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前3條

1 周茜,趙明生,扈e，

本文編號(hào)：2304693