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Multi-application personalization: Data propagation evaluation on a real-life search query log

机译:多应用程序个性化:真实搜索查询日志中的数据传播评估

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摘要

In the field of multi-application personalization, several techniques have been proposed to support user modeling. None of them have sufficiently investigated the opportunity for a multi-application profile to evolve over time in order to avoid data inconsistency and the subsequent loss of income for website users and companies. In this paper, we propose a model addressing this issue and we focus in particular on user profile data propagation management. Data propagation is a way to reduce the amount of inconsistent user profile information over several applications, even in the case of temporary coalitions of applications as happens in Digital Ecosystems. To evaluate our model, we first extract user profiles using logs of the large real-life AOL search engine. Then, we simulate data propagation along semantically related user information.
机译:在多应用程序个性化领域,已经提出了几种支持用户建模的技术。他们中没有一个人充分研究了多应用程序配置文件随时间变化的机会,以避免数据不一致以及随之而来的网站用户和公司收入损失。在本文中,我们提出了一个解决此问题的模型,并且特别关注于用户配置文件数据传播管理。数据传播是一种减少多个应用程序中不一致的用户配置文件信息量的方法,即使在数字生态系统中发生应用程序临时联盟的情况下也是如此。为了评估我们的模型,我们首先使用大型现实AOL搜索引擎的日志提取用户个人资料。然后,我们沿着语义相关的用户信息模拟数据传播。

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