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首页> 外文期刊>Malaysian Journal of Computer Science >Learning Ranking Functions For Information Retrieval Using Layered Multi-Population Genetic Programming
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Learning Ranking Functions For Information Retrieval Using Layered Multi-Population Genetic Programming

机译：使用分层多种群遗传规划学习信息检索的排名函数

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摘要

Ranking plays a key role in many applications, such as document retrieval, recommendation, question answering, and machine translation. In practice, a ranking function (or model) is exploited to determine the rank-order relations between objects, with respect to a particular criterion. In this paper, a layered multipopulation genetic programming based method, known as RankMGP, is proposed to learn ranking functions for document retrieval by incorporating various types of retrieval models into a singular one with high effectiveness. RankMGP represents a potential solution (i.e., a ranking function) as an individual in a population of genetic programming and aims to directly optimize information retrieval evaluation measures in the evolution process. Overall, RankMGP consists of a set of layers and a sequential workflow running through the layers. In one layer, multiple populations evolve independently to generate a set of the best individuals. When the evolution process is completed, a new training dataset is created using the best individuals and the input training set of the layer. Then, the populations in the next layer evolve with the new training dataset. In the final layer, the best individual is obtained as the output ranking function. The proposed method is evaluated using the LETOR datasets and is found to be superior to classical information retrieval models, such as Okapi BM25. It is also statistically competitive with the state-of-the-art methods, including Ranking SVM, ListNet, AdaRank and RankBoost.
机译：排名在许多应用程序中起着关键作用，例如文档检索，推荐，问题解答和机器翻译。在实践中，利用排名函数（或模型）来确定对象之间相对于特定标准的排名关系。在本文中，提出了一种基于分层多种群遗传规划的方法，称为RankMGP，该方法通过将各种类型的检索模型合并到单个有效的模型中来学习文档检索的排序功能。 RankMGP作为遗传程序设计群体中的一个个体，代表了一种潜在的解决方案（即排序功能），旨在在进化过程中直接优化信息检索评估措施。总体而言，RankMGP由一组图层和贯穿这些图层的顺序工作流程组成。在一个层次上，多个种群独立地进化以产生一组最佳个体。演化过程完成后，将使用最佳个人和该层的输入训练集来创建新的训练数据集。然后，下一层的种群将随新的训练数据集一起演化。在最后一层，获得最佳个体作为输出排名函数。使用LETOR数据集对提出的方法进行了评估，发现该方法优于经典的信息检索模型，例如Okapi BM25。与最新的方法（包括Rank SVM，ListNet，AdaRank和RankBoost）相比，它在统计上也具有竞争力。

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