Yael Brumer

The WSDM Cup 2017 Triple scoring challenge is aimed at calculating and assigning relevance scores for triples from type-like
relations. Such scores are a fundamental ingredient for ranking
results in entity search. In this paper, we propose a method that uses
neural embedding techniques to accurately calculate an entity score
for a triple based on its nearest neighbor. We strive to develop a new latent semantic model with a deep structure that captures the semantic and syntactic… Show more

The WSDM Cup 2017 Triple scoring challenge is aimed at calculating and assigning relevance scores for triples from type-like
relations. Such scores are a fundamental ingredient for ranking
results in entity search. In this paper, we propose a method that uses
neural embedding techniques to accurately calculate an entity score
for a triple based on its nearest neighbor. We strive to develop a new latent semantic model with a deep structure that captures the semantic and syntactic relations between words. Show less

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… Show more

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.
Show less