Product and content personalization is now ubiquitous in e-commerce. Available transactional data is typically too sparse 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 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 datasets and provides a significant performance improvement over state of the art incumbent approaches to tensor recovery. Further, 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.
Vivek is interested in the development of new methodologies for large scale dynamic optimization and applications in revenue management, finance, marketing and healthcare. He received his Ph.D. in Electrical Engineering from Stanford University in 2007 and has been at MIT since. Vivek is a recipient of an IEEE Region 6 Undergraduate Student Paper Prize (2002), an INFORMS MSOM Student Paper Prize (2006), an MIT Solomon Buchsbaum Award (2008), an INFORMS JFIG paper prize twice (2009, 2011), the NSF CAREER award (2011), MIT Sloan’s Outstanding Teacher award (2013), the INFORMS Simulation Society Best Publication Award (2014), the INFORMS Pricing and Revenue Management Best Publication Award (2015), and the INFORMS MSOM Best Paper in Management Science (2016). Outside of academia, he has contributed to the design of the algorithmic trading strategies of GMO's (a USD 100B + money manager) first high frequency venture and is the co-founder and CTO of Celect, a venture-backed predictive analytics startup.