Fair and balanced: learning to present news stories
Fair and balanced: learning to present news stories
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公平和平衡：学习呈现新闻报道
DOI：
10.1145/2124295.2124337
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Alex Smola
中科院分区：
文献类型：
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作者：
Amr Ahmed;C. Teo;S. Vishwanathan;Alex Smola
相关性、多样性和个性化是呈现易于引起用户兴趣的内容时的关键问题。在呈现一组引人入胜的新闻故事时尤其如此。在本文中，我们提出了一个有效的算法，从流媒体新闻语料库中选择一个小的相关文章的子集。它提供了三个关键的改进，在过去的工作：1）它是基于一个详细的模型，用户的观看行为，不需要明确的反馈。2)我们使用子模块化的概念来估计与内容交互的倾向。这改进了经典的上下文无关的相关性排名算法。与现有方法不同，我们从数据中学习子模函数。3)我们提出了一个有效的在线算法，可以适应个性化，故事改编和分解模型。实验表明，我们的系统产生了显着的改善，在生产中部署的检索系统。
Relevance, diversity and personalization are key issues when presenting content which is apt to pique a user's interest. This is particularly true when presenting an engaging set of news stories. In this paper we propose an efficient algorithm for selecting a small subset of relevant articles from a streaming news corpus. It offers three key pieces of improvement over past work: 1) It is based on a detailed model of a user's viewing behavior which does not require explicit feedback. 2) We use the notion of submodularity to estimate the propensity of interacting with content. This improves over the classical context independent relevance ranking algorithms. Unlike existing methods, we learn the submodular function from the data. 3) We present an efficient online algorithm which can be adapted for personalization, story adaptation, and factorization models. Experiments show that our system yields a significant improvement over a retrieval system deployed in production.