新闻推荐的addressa数据集

J. Gulla, Lemei Zhang, Peng Liu, Özlem Özgöbek, Xiaomeng Su
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引用次数: 138

摘要

推荐系统的数据集很少,而且往往不足以满足新闻推荐的情境化性质。新闻推荐系统依赖于时间和地点,使用隐式信号,通常包括协作和基于内容的组件。在本文中,我们引入了支持所有这些方面的新闻推荐的addressa压缩新闻数据集。该数据集有两个版本,大型的2000万数据集记录了Adresseavisen新闻门户网站10周的流量,而小型的200万数据集只记录了一周的流量。我们解释了数据集的结构,并讨论了如何将其用于高级新闻推荐系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Adressa dataset for news recommendation
Datasets for recommender systems are few and often inadequate for the contextualized nature of news recommendation. News recommender systems are both time- and location-dependent, make use of implicit signals, and often include both collaborative and content-based components. In this paper we introduce the Adressa compact news dataset, which supports all these aspects of news recommendation. The dataset comes in two versions, the large 20M dataset of 10 weeks' traffic on Adresseavisen's news portal, and the small 2M dataset of only one week's traffic. We explain the structure of the dataset and discuss how it can be used in advanced news recommender systems.
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