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User Involvement in Automatic Filtering: An Experimental Study

机译：用户参与自动过滤的实验研究

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摘要

The aim in information filtering is to provide users with a personalised selection of information, based on their interest profile. In adaptive information filtering, this profile partially or completely acquired by automatic means. This paper investigates if profile generation can be partially acquired by automatic methods and partially by direct user involvement. The issue is explored through an empirical study of a simulated filtering system that mixes automatic and manual profile generation. The study covers several issues involved in mixed control. The first issue concerns if a machine-learned profile can provide better filtering performance if generated from an initial explicit user profile. The second issue concerns if user involvement can improve on a system-generated or adapted profile. Finally, the relationship between filtering performance and user ratings is investigated. In this particular study the initial setup of a personal profile was effective and yielded performance improvements that persisted after substantiate training. However, the study showed no correlation between users' ratings of profiles and profile filtering performance, and only weak indications that users could improve profiles that already had been trained on feedback.
机译：信息过滤的目的是根据用户的兴趣状况为用户提供个性化的信息选择。在自适应信息过滤中，此配置文件通过自动方式部分或完全获取。本文研究了是否可以通过自动方法部分获取配置文件生成，以及是否可以通过直接用户参与来获取配置文件生成。通过对混合自动和手动配置文件生成的模拟过滤系统进行实证研究，探索了该问题。该研究涵盖了混合控制中涉及的几个问题。第一个问题涉及如果从初始显式用户配置文件生成的机器学习的配置文件是否可以提供更好的过滤性能。第二个问题涉及用户的参与是否可以改善系统生成的或适应的配置文件。最后，研究了过滤性能与用户评价之间的关系。在这项特殊的研究中，个人档案的初始设置是有效的，并且在进行实质性培训后仍能持续改善绩效。但是，该研究表明用户的个人资料评级与个人资料过滤性能之间没有关联，只有微弱的迹象表明用户可以改善已经接受反馈培训的个人资料。

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