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首页> 外文会议>Database and Expert Systems Applications; Lecture Notes in Computer Science; 4080 >User Preference Modeling Based on Interest and Impressions for News Portal Site Systems
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User Preference Modeling Based on Interest and Impressions for News Portal Site Systems

机译：基于兴趣和印象的新闻门户网站系统用户偏好建模

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摘要

We have developed an application called My Portal Viewer (MPV) that effectively integrates many articles collected from multiple news sites and presents these integrations through a familiar interface such as a page the user has often visited. MPV dynamically determines keywords of interest that a user might potentially be interested in based on the history of the articles the user has read and creates categories based on these interest words. MPV and many other similar integration systems, however, cause problems where users cannot find only their interest articles in each category because they are only ranked by frequency and the cooccurrence of keywords. We propose a new method of selecting further articles from each category using a user's impressions of articles. The improved MPV, called MPV Plus, selects and recommends more desirable articles using the method we propose. This paper presents the design concept and process flow of MPV Plus and reports on its effectiveness as evaluated in experiments.
机译：我们开发了一个名为My Portal Viewer（MPV）的应用程序，该应用程序有效地集成了从多个新闻站点收集的许多文章，并通过熟悉的界面（例如用户经常访问的页面）展示了这些集成。 MPV根据用户已阅读文章的历史记录动态确定用户可能感兴趣的兴趣关键字，并根据这些兴趣词创建类别。但是，MPV和许多其他类似的集成系统会引起问题，用户无法仅在每个类别中找到他们感兴趣的文章，因为它们仅按频率和关键字的同时出现而排名。我们提出了一种使用用户对文章的印象从每个类别中选择其他文章的新方法。改进的MPV（称为MPV Plus）使用我们提出的方法来选择并推荐更理想的商品。本文介绍了MPV Plus的设计概念和流程，并报告了其在实验中评估的有效性。

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