Wang, X;
Yu, L;
Ren, K;
Tao, G;
Zhang, W;
Yu, Y;
Wang, J;
(2017)
Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration.
In:
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
(pp. pp. 2051-2059).
ACM
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Abstract
As aggregators, online news portals face great challenges in continuously selecting a pool of candidate articles to be shown to their users. Typically, those candidate articles are recommended manually by platform editors from a much larger pool of articles aggregated from multiple sources. Such a hand-pick process is labor intensive and time-consuming. In this paper, we study the editor article selection behavior and propose a learning by demonstration system to automatically select a subset of articles from the large pool. Our data analysis shows that (i) editors' selection criteria are non-explicit, which are less based only on the keywords or topics, but more depend on the quality and attractiveness of the writing from the candidate article, which is hard to capture based on traditional bag-of-words article representation. And (ii) editors' article selection behaviors are dynamic: articles with different data distribution come into the pool everyday and the editors' preference varies, which are driven by some underlying periodic or occasional patterns. To address such problems, we propose a meta-attention model across multiple deep neural nets to (i) automatically catch the editors' underlying selection criteria via the automatic representation learning of each article and its interaction with the meta data and (ii) adaptively capture the change of such criteria via a hybrid attention model. The attention model strategically incorporates multiple prediction models, which are trained in previous days. The system has been deployed in a commercial article feed platform. A 9-day A/B testing has demonstrated the consistent superiority of our proposed model over several strong baselines.
| Type: | Proceedings paper |
|---|---|
| Title: | Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration |
| Event: | The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
| Location: | Halifax (NS), Canada |
| Dates: | 13th-17th August 2017 |
| ISBN-13: | 978-1-4503-4887-4 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3097983.3098096 |
| Publisher version: | http://doi.org/10.1145/3097983.3098096 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Recommendation, Learning by Demonstration, Attention Models, Convolutional Neural Network |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10066102 |
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