Search Results for author:
Found 2 papers, 2 papers with code
Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling
Recommender systems are indispensable in the realm of online applications, and sequential recommendation has enjoyed considerable prevalence due to its capacity to encapsulate the dynamic shifts in user interests.
TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders
In this work, we propose TimeMAE, a novel self-supervised paradigm for learning transferrable time series representations based on transformer networks.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
