Learning Preferences with Side Information
研究了在电子商务中利用产品与客户交互的辅助信息来恢复大规模偏好矩阵的问题，提出了一种高效的张量切片恢复算法，在音乐流媒体数据上验证了性能提升。
Product and content personalization is now ubiquitous in e-commerce. There are typically not enough available transactional data for this task. As such, companies today seek to use a variety of information on the interactions between a product and a customer to drive personalization decisions. We formalize this problem as one of recovering a large-scale matrix with side information in the form of additional matrices of conforming dimension. Viewing the matrix we seek to recover and the side information we have as slices of a tensor, we consider the problem of slice recovery, which is to recover specific slices of “simple” tensors from noisy observations of the entire tensor. We propose a definition of simplicity that on the one hand elegantly generalizes a standard generative model for our motivating problem and on the other hand subsumes low-rank tensors for a variety of existing definitions of tensor rank. We provide an efficient algorithm for slice recovery that is practical for massive data sets and provides a significant performance improvement over state-of-the-art incumbent approaches to tensor recovery. Furthermore, we establish near-optimal recovery guarantees that, in an important regime, represent an order improvement over the best available results for this problem. Experiments on data from a music streaming service demonstrate the performance and scalability of our algorithm. The e-companion is available at https://doi.org/10.1287/mnsc.2018.3092 . This paper was accepted by Noah Gans, stochastic models and simulation.