Scalable distributed inference of dynamic user interests for behavioral targeting
Scalable distributed inference of dynamic user interests for behavioral targeting
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DOI：
10.1145/2020408.2020433
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发表时间：
2011-08
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通讯作者：
Amr Ahmed;Yucheng Low;M. Aly;V. Josifovski;Alex Smola
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文献类型：
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作者：
Amr Ahmed;Yucheng Low;M. Aly;V. Josifovski;Alex Smola
历史用户活动是构建用户简档的关键，以预测许多Web应用程序中的用户行为和亲和力，例如在线广告的定位，内容个性化和社交推荐。用户简档是暂时的，并且用户活动模式的变化对于改进预测和推荐特别有用。例如，在汽车相关的网页的兴趣增加，很可能表明用户可能会购买一个新的vehicle.In本文中，我们提出了一个全面的统计框架，用户分析的基础上，主题模型，能够捕捉到这样的效果在一个完全无监督的方式。我们的方法模型的主题兴趣的用户动态的用户关联的主题和主题本身都允许随着时间的推移而变化，从而确保配置文件保持当前。我们描述了一个流，分布式推理算法，它能够处理数以千万计的用户。我们的研究结果表明，我们的模型有助于改善行为定位的显示广告相对于基线模型，不包括主题和/或时间的依赖关系。作为一个副作用，我们的模型产生了人类可以理解的结果，广告商可以直观地使用这些结果。
Historical user activity is key for building user profiles to predict the user behavior and affinities in many web applications such as targeting of online advertising, content personalization and social recommendations. User profiles are temporal, and changes in a user's activity patterns are particularly useful for improved prediction and recommendation. For instance, an increased interest in car-related web pages may well suggest that the user might be shopping for a new vehicle.In this paper we present a comprehensive statistical framework for user profiling based on topic models which is able to capture such effects in a fully \emph{unsupervised} fashion. Our method models topical interests of a user dynamically where both the user association with the topics and the topics themselves are allowed to vary over time, thus ensuring that the profiles remain current. We describe a streaming, distributed inference algorithm which is able to handle tens of millions of users. Our results show that our model contributes towards improved behavioral targeting of display advertising relative to baseline models that do not incorporate topical and/or temporal dependencies. As a side-effect our model yields human-understandable results which can be used in an intuitive fashion by advertisers.