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.
|Journal||CEUR Workshop Proceedings|
|State||Published - 2016|
|Event||10th ACM Conference on Recommender Systems, RecSys 2016 - Boston, United States|
Duration: 17 Sep 2016 → …
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- Collaborative filtering
- Recommender systems