Predicting web actions from HTML content
Predicting web actions from HTML content
复制标题
根据 HTML 内容预测 Web 操作
DOI：
10.1145/513338.513380
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
Brian D. Davison
中科院分区：
文献类型：
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作者：
Brian D. Davison
大多数提出的Web预取技术根据对请求对象的历史引用进行预测。与此相反，本文研究的准确性预测用户的下一个动作的基础上分析的内容，最近由用户请求的页面。预测使用的模型的用户的兴趣和周围的超文本锚最近请求的网页的文本的相似性。这种方法可以预测用户从未采取过的行动，并可能做出反映当前用户兴趣的预测。我们使用来自Web活动的完整内容日志的数据来评估这种技术，并发现基于文本相似性的预测优于更简单的方法。
Most proposed Web prefetching techniques make predictions based on the historical references to requested objects. In contrast, this paper examines the accuracy of predicting a user's next action based on analysis of the content of the pages requested recently by the user. Predictions are made using the similarity of a model of the user's interest to the text in and around the hypertext anchors of recently requested Web pages. This approa22ch can make predictions of actions that have never been taken by the user and potentially make predictions that reflect current user interests. We evaluate this technique using data from a full-content log of Web activity and find that textual similarity-based predictions outperform simpler approaches.