Today, intelligent user interfaces on the web often come in form of recommendation services tailoring content to individual users. Recommendation of web content such as news articles often requires a certain amount of explicit ratings to allow for satisfactory results, i.e., the selection of content actually relevant for the user. Yet, the collection of such explicit ratings is time-consuming and dependent on users' willingness to provide the required information on a regular basis. Thus, using implicit interest indicators can be a helpful complementation to relying on explicitly entered information only. Analysis of reading behavior on the web can be the basis for the derivation of such implicit indicators. Previous work has already identified several indicators and discussed how they can be used as a basis for user models. However, most earlier work is either of conceptual nature and does not involve studies to prove the suggested concepts or relies on meanwhile potentially outdated technology. All earlier discussions of the topic further have in common that they do not yet consider mobile contexts. This paper builds upon earlier work, however providing a major update regarding technology and web reading context, distinguishing between desktop and mobile settings. This update also allowed us to identify a set of new indicators that so far have not yet been discussed. This paper describes (i) our technical work, a framework for analyzing user interactions with the browser relying on latest web technologies, (ii) the implicit interest indicators we either revisited or newly identified, and (iii) the results of an online study on web reading behavior as a basis for derivation of interest we conducted with 96 participants.
翻译:今天,网上的智能用户界面往往以建议服务的形式出现,对内容进行量身定制,向个别用户提供内容; 诸如新闻文章等网络内容的建议往往要求一定数量的明确评级,以取得令人满意的结果,即选择与用户实际相关的内容; 然而,收集这种明确评级很费时,而且取决于用户是否愿意定期提供所需信息。因此,使用隐含兴趣的指标可以有助于补充仅依靠明确输入的信息; 分析网上阅读行为可以是得出这种隐含指标的基础。 先前的工作已经确定了若干指标,并讨论了如何将这些指标用作用户模型的基础。然而,大多数早期的工作要么具有概念性质,并不涉及研究以证明所建议的概念,要么依靠可能过时的技术。关于这一专题的所有早期讨论都与用户是否愿意定期提供所需信息有关,因此,它们尚未考虑移动背景。 本文件以先前的工作为基础,但提供技术和网络阅读背景方面的主要更新,可以区分桌面和移动环境。 更新后,我们还可以确定一套新的指标,迄今为止尚未作为用户模型的基础。