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首页> 外文会议>2017 IEEE SmartWorld, Ubiquitous Intelligence amp; Computing, Advanced amp; Trusted Computed, Scalable Computing amp; Communications, Cloud amp; Big Data Computing, Internet of People and Smart City Innovation >A context-aware recommendation system for improving the performance of targeted mobile advertising
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A context-aware recommendation system for improving the performance of targeted mobile advertising

机译：一种上下文感知的推荐系统，用于改善目标移动广告的性能

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摘要

Mobile advertising has evolved into an important category of interactive advertising because it enables advertisers to target users considering contextual factors (location, activities, devices etc.). Logically, this makes mobile applications better advertising platform to distribute advertisement enhanced by recommendation system. A recommendation system can efficiently suggest the most appropriate content of interest to users according to their preferences. Few prior studies have tried to incorporate context-awareness into the recommendation system particularly in domain of mobile advertising. A key challenge is complexity of mobile contextual information and scalability of required algorithms. This study presents context-aware collaborative filtering algorithms improving the relevancy of the prediction results. We first define context-awareness of mobile advertising scenario, and then apply the context similarity to measure a novel user-context - advertisement model with tensor factorization. We propose an algorithmic extension of multiple-dimensional collaborative filtering to show that our proposed system can outperform to this problem.
机译：移动广告已发展成为交互式广告的重要类别，因为它使广告商可以考虑上下文因素（位置，活动，设备等）来定位用户。从逻辑上讲，这使移动应用程序成为更好的广告平台来分发由推荐系统增强的广告。推荐系统可以根据用户的偏好来有效地建议用户最感兴趣的内容。很少有先前的研究尝试将上下文意识纳入推荐系统中，特别是在移动广告领域。一个关键的挑战是移动上下文信息的复杂性和所需算法的可伸缩性。这项研究提出了上下文感知协作过滤算法，提高了预测结果的相关性。我们首先定义移动广告场景的上下文感知，然后将上下文相似度应用于具有张量分解的新型用户上下文广告模型。我们提出了多维协作过滤的算法扩展，以表明我们提出的系统可以胜过该问题。

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