Learn to rank Persian web content based on multi-layered genetic programming

Abstract

Learning to rank (L2R) has emerged as a promising approach in handling the existing challenges of Web search engines. However, there are major drawbacks with the present learning to rank techniques. Current L2R algorithms do not take into account to the search behavior of the users embedded in their search sessions’ logs. On the other hand, machine-learning as a data-intensive process requires a large volume of data about users’ queries as well as Web documents. This situation has made the usage of L2R techniques questionable in the real-world applications. Recently, by the use of the click-through data model and based on the generation of click-through features, a novel approach is proposed, named as MGP-Rank. Using the layered genetic-programming model, MGP-Rank has achieved noticeable performance on the ranking of the English Web content. In this study, with respect to the specific characteristics of the Persian language, some suitable scenarios are presented for the generation of the click-through features. In this way, a customized version of the MGP-Rank is proposed of the Persian Web retrieval. The evaluation results of this algorithm on the dotIR dataset, indicate its considerable improvement in comparison with major ranking methods. The improvement of the performance is particularly more noticeable in the top part of the search results lists, which are most frequently visited by the Web users.