Understanding temporal dynamics has proved to be highly valuable for accurate
recommendation. Sequential recommenders have been successful in modeling the …

Recommender System research suffers currently from a disconnect between the size of
academic data sets and the scale of industrial production systems. In order to bridge that …

Characterizing temporal dependence patterns is a critical step in understanding the
statistical properties of sequential data. Long Range Dependence (LRD)---referring to long …

The ability to capture Long Range Dependence (LRD) in a stochastic process is of prime
importance in the context of predictive models. A sequential model with a longer-term …

Recommender system research suffers from a disconnect between the size of academic
data sets and the scale of industrial production systems. In order to bridge that gap, we …

Designing neural networks for continuous-time stochastic processes is challenging,
especially when observations are made irregularly. In this article, we analyze neural …

Across the web and mobile applications, recommender systems are relied upon to surface
the right items to users at the right time. This implies user preferences are usually dynamic in …

Randomized Fractal Expansions for Production-Scale Public Collaborative-Filtering Data Sets
Jump to Content Research Research Who we are Back to Who we are menu Defining the …