个性化的注意力跨度

摘要：

点击一个项目可以说是推荐系统中使用最广泛的功能。然而，点击是浏览器可以触发的174个事件之一。本文提出了一个框架，有效地收集和存储数据的事件流。提供了一组挖掘方法来提取用户参与特征，例如：注意广度，滚动深度和可见印象。在这项工作中，我们提出了一个实验，其中基于注意广度的建议比点击率高出340%。

英文摘要：

A click on an item is arguably the most widely used feature in recommender systems. However, a click is one out of 174 events a browser can trigger. This paper presents a framework to effectively collect and store data from event streams. A set of mining methods is provided to extract user engagement features such as: attention span, scrolling depth and visible impressions. In this work, we present an experiment where recommendations based on attention span drove 340% higher click-through-rate than clicks.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。