基于上下文感知的广告推荐,支持高速社交新闻推送

Yuchen Li, Dongxiang Zhang, Ziquan Lan, K. Tan
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引用次数: 35

摘要

社交媒体广告是一个价值数十亿美元的市场,已成为Facebook和Twitter的主要收入来源。为了向潜在感兴趣的用户投放广告,这些社交网络平台根据每个用户的个人兴趣学习了一个预测模型。然而,由于用户兴趣往往演变缓慢,用户最终可能会收到重复的广告。在本文中,我们提出了一个上下文感知广告框架,该框架考虑了相对静态的个人兴趣以及来自朋友的动态新闻馈送,以推动广告点击率的增长。为了满足实时需求,我们首先提出了一种在线检索策略,当读取操作被触发时,该策略可以找到与动态上下文匹配的k个最相关的广告。为了避免上下文变化不大时频繁检索,我们提出了一种安全区域方法来快速确定用户的前k个广告是否发生了变化。最后,我们提出了一个混合模型,通过分析新闻源的动态特性,将两种方法的优点结合起来,以确定合适的检索策略。在多个真实社交网络和广告数据集上进行的大量实验验证了我们的混合模型的效率和鲁棒性。
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Context-aware advertisement recommendation for high-speed social news feeding
Social media advertising is a multi-billion dollar market and has become the major revenue source for Facebook and Twitter. To deliver ads to potentially interested users, these social network platforms learn a prediction model for each user based on their personal interests. However, as user interests often evolve slowly, the user may end up receiving repetitive ads. In this paper, we propose a context-aware advertising framework that takes into account the relatively static personal interests as well as the dynamic news feed from friends to drive growth in the ad click-through rate. To meet the real-time requirement, we first propose an online retrieval strategy that finds k most relevant ads matching the dynamic context when a read operation is triggered. To avoid frequent retrieval when the context varies little, we propose a safe region method to quickly determine whether the top-k ads of a user are changed. Finally, we propose a hybrid model to combine the merits of both methods by analyzing the dynamism of news feed to determine an appropriate retrieval strategy. Extensive experiments conducted on multiple real social networks and ad datasets verified the efficiency and robustness of our hybrid model.
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