大规模定向广告系统的多流形学习
Multi-Manifold Learning for Large-scale Targeted Advertising System
摘要：
信使广告（广告）提供直接和个人的用户体验，从而产生高转化率和销售。然而，人们对广告持怀疑态度，有时将其视为垃圾邮件，最终导致用户满意度下降。强烈需要有针对性的广告，即向可能对特定广告信息感兴趣的个人提供广告。精确用户定位成功的关键在于学习嵌入空间中的准确用户和广告表示。以前的大多数研究都限制了在欧几里得空间中的表示学习，但最近的研究表明，双曲流形学习的复杂网络属性的不同投影出现在现实世界的数据集，如社交网络，推荐系统和广告。我们提出了一个框架，可以有效地学习在双曲空间上的用户和广告的层次结构，并扩展到多流形学习。我们的方法构造多个双曲流形与可学习的曲率和映射的表示用户和广告到每个流形。每个流形的原点被设置为每个用户聚类的质心。使用双曲空间中两个实体之间的距离来估计每个广告的用户偏好，并且通过聚合从学习的多个流形计算的值来确定最终预测。我们在公共基准数据集和大型商业信使系统LINE上评估了我们的方法，并通过提高性能证明了其有效性。
英文摘要：
Messenger advertisements (ads) give direct and personal user experience yielding high conversion rates and sales. However, people are skeptical about ads and sometimes perceive them as spam, which eventually leads to a decrease in user satisfaction. Targeted advertising, which serves ads to individuals who may exhibit interest in a particular advertising message, is strongly required. The key to the success of precise user targeting lies in learning the accurate user and ad representation in the embedding space. Most of the previous studies have limited the representation learning in the Euclidean space, but recent studies have suggested hyperbolic manifold learning for the distinct projection of complex network properties emerging from real-world datasets such as social networks, recommender systems, and advertising. We propose a framework that can effectively learn the hierarchical structure in users and ads on the hyperbolic space, and extend to the Multi-Manifold Learning. Our method constructs multiple hyperbolic manifolds with learnable curvatures and maps the representation of user and ad to each manifold. The origin of each manifold is set as the centroid of each user cluster. The user preference for each ad is estimated using the distance between two entities in the hyperbolic space, and the final prediction is determined by aggregating the values calculated from the learned multiple manifolds. We evaluate our method on public benchmark datasets and a large-scale commercial messenger system LINE, and demonstrate its effectiveness through improved performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
