Covariate-Assisted Sparse Tensor Completion
协变量辅助的稀疏张量补全AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究广告点击率的协变量辅助张量补全，没有明确采用离散选择模型或开展所列经济学领域的识别分析。
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协变量辅助的稀疏张量补全
- 研究问题
- 当张量既高度缺失又包含大量零值时，如何利用沿各维度可获得的协变量，提高潜在成分恢复和张量补全精度？
- 方法与思路
- 作者提出 COSTCO，联合提取张量与协变量矩阵中的潜在成分，学习综合表示，用于广告点击率张量的恢复。
- 材料陈述的贡献
- 摘要称给出成分恢复误差界并量化协变量对观测概率条件及精度的改善；广告应用报告相对基线的准确性提高百分之二十三。
- 阅读关注点
- 应核对张量与协变量共享成分的条件、缺失机制和观测概率要求，以及应用中准确性指标、基线和验证划分。
- 阅读边界
- 仅据版本未注明的索引摘要；百分之二十三为作者报告的特定应用结果，摘要未说明指标定义，不能外推为普遍补全增益。
原文摘要
We aim to provably complete a sparse and highly missing tensor in the presence of covariate information along tensor modes. Our motivation comes from online advertising where users’ click-through-rates (CTR) on ads over various devices form a CTR tensor that has about 96% missing entries and has many zeros on nonmissing entries, which makes the standalone tensor completion method unsatisfactory. Beside the CTR tensor, additional ad features or user characteristics are often available. In this article, we propose Covariate-assisted Sparse Tensor Completion (COSTCO) to incorporate covariate information for the recovery of the sparse tensor. The key idea is to jointly extract latent components from both the tensor and the covariate matrix to learn a synthetic representation. Theoretically, we derive the error bound for the recovered tensor components and explicitly quantify the improvements on both the reveal probability condition and the tensor recovery accuracy due to covariates. Finally, we apply COSTCO to an advertisement dataset consisting of a CTR tensor and ad covariate matrix, leading to 23% accuracy improvement over the baseline. An important by-product is that ad latent components from COSTCO reveal interesting ad clusters, which are useful for better ad targeting. Supplementary materials for this article are available online.