English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The goal of sequential event prediction is to estimate the next event basedon a sequence of historical events, with applications to sequentialrecommendation, user behavior analysis and clinical treatment. In practice, thenext-event prediction models are trained with sequential data collected at onetime and need to generalize to newly arrived sequences in remote future, whichrequires models to handle temporal distribution shift from training to testing.In this paper, we first take a data-generating perspective to reveal a negativeresult that existing approaches with maximum likelihood estimation would failfor distribution shift due to the latent context confounder, i.e., the commoncause for the historical events and the next event. Then we devise a newlearning objective based on backdoor adjustment and further harness variationalinference to make it tractable for sequence learning problems. On top of that,we propose a framework with hierarchical branching structures for learningcontext-specific representations. Comprehensive experiments on diverse tasks(e.g., sequential recommendation) demonstrate the effectiveness, applicabilityand scalability of our method with various off-the-shelf models as backbones.

Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment