Modelling session activity with neural embedding

Oren Barkan, Yael Brumer, Noam Koenigstein

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


Neural embedding techniques are being applied in a growing number of machine learning applications. In this work, we demonstrate a neural embedding technique to model users' session activity. Specifically, we consider a dataset collected from Microsoft's App Store consisting of user sessions that include sequential click actions and item purchases. Our goal is to learn a latent manifold that captures users' session activity and can be utilized for contextual recommendations in an online app store.

Original languageEnglish
JournalCEUR Workshop Proceedings
StatePublished - 2016
Externally publishedYes
Event10th ACM Conference on Recommender Systems, RecSys 2016 - Boston, United States
Duration: 15 Sep 201619 Sep 2016


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