TY - JOUR
T1 - Modelling session activity with neural embedding
AU - Barkan, Oren
AU - Brumer, Yael
AU - Koenigstein, Noam
N1 - Publisher Copyright:
Copyright held by the author(s).
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Collaborative filtering
KW - Recommender systems
KW - Skip-Gram
UR - http://www.scopus.com/inward/record.url?scp=84991059370&partnerID=8YFLogxK
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AN - SCOPUS:84991059370
SN - 1613-0073
VL - 1688
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 10th ACM Conference on Recommender Systems, RecSys 2016
Y2 - 15 September 2016 through 19 September 2016
ER -