Mobile Search through Dynamic Mashups

Diego Fabian Gomez-Pardo, Franklin E. Navia-Urbano, Luis Javier Suarez Meza, J. Corrales
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Abstract

Nowadays, mobile devices are the first choice for seeking information and content consumption on the Web. However, the overwhelming amount of available web resources, significantly affects the quality of the results returned by search systems. Traditionally, the web resource's retrieval is performed by using syntactic and/or semantic matches between the user query and content of the resources, leaving aside aspects such as: Goals and intentions that an end-user has when performing a query. This paper introduces a novel approach that allows to improve the mobile search user experience, delivering results according to his goals and intentions. This proposal is based on three main processes: i) To infer goals and intentions of end-users from their search query through a probabilistic generative model called LDA (Latent Dirichlet Allocation). ii) to discover resources based on inferred goals and intentions. And iii) to generate a dynamic mash up with the retrieved resources. We argue that the concept of mash up can contribute to improve the user experience in mobile search. Experiments show promising results in the user search experience in contrast to traditional approaches.
通过动态mashup进行移动搜索
如今,移动设备是人们在网络上寻找信息和消费内容的首选设备。然而,大量可用的网络资源极大地影响了搜索系统返回结果的质量。传统上,web资源的检索是通过使用用户查询和资源内容之间的语法和/或语义匹配来执行的,而不考虑最终用户在执行查询时的目标和意图。本文介绍了一种新颖的方法,可以改善移动搜索用户体验,根据用户的目标和意图提供结果。该建议基于三个主要过程:i)通过称为LDA(潜在狄利克雷分配)的概率生成模型从最终用户的搜索查询推断目标和意图。Ii)根据推断的目标和意图发现资源。iii)生成与检索到的资源的动态混搭。我们认为,混搭的概念可以帮助改善移动搜索的用户体验。与传统方法相比,实验显示了在用户搜索体验方面有希望的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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