The Adressa Dataset for News Recommendation

被引：119

作者：

Gulla, Jon Atle ^{[1
]}

Zhang, Lemei ^{[1
]}

Liu, Peng ^{[1
]}

Ozgobek, Ozlem ^{[1
]}

Su, Xiaomeng ^{[1
]}

机构：

[1] NTNU, Dept Comp Sci, Trondheim, Norway

来源：

2017 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2017) | 2017年

关键词：

Datasets; recommender systems; machine learning;

D O I：

10.1145/3106426.3109436

中图分类号：

TP18 [人工智能理论];

学科分类号：

140502 [人工智能];

摘要：

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.

引用

页码：1042 / 1048

页数：7

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