基于模糊语言查询的多目标遗传算法的用户轮廓学习

O. Cordón, E. Herrera-Viedma, M. Luque
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引用次数: 0

摘要

本文提出了一种多目标遗传算法,用于文本检索中持久模糊语言查询的自动学习。这些查询能够以一种比经典的“字包”用户概要结构更易于解释的方式表示用户的长期信息需求。由于其多目标特性,引入的遗传模糊系统能够在一次运行中为相同的信息需求构建不同的查询,并在精度和召回率之间进行不同的权衡。在经典的ccm集合上进行的实验表明,尽管我们的遗传模糊系统获得的不同查询在检索任务中的准确性低于一种最先进的词袋方法,但它们构成了更灵活、可理解和富有表现力的用户档案
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fuzzy Linguistic Query-based User Profile Learning by Multiobjective Genetic Algorithms
In this paper, a multiobjective genetic algorithm is proposed to automatically learn persistent fuzzy linguistic queries for text retrieval applications. These queries are able to represent user's long-term standing information needs in a more interpretable way than the classical "bag of words" user profile structure. Thanks to its multiobjective nature, the introduced genetic fuzzy system is able to build different queries for the same information need in a single run, with a different trade-off between precision and recall. The experiments performed on the classical CACM collection show that although the different queries obtained from our genetic fuzzy system are less accurate in the retrieval task than those derived by one state-of-the-art bag of words method, they compose more flexible, comprehensible and expressive user profiles
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