O
Oren Barkan
Researcher at Microsoft
Publications - 58
Citations - 1259
Oren Barkan is an academic researcher from Microsoft. The author has contributed to research in topics: Recommender system & Collaborative filtering. The author has an hindex of 13, co-authored 53 publications receiving 988 citations. Previous affiliations of Oren Barkan include Tel Aviv University & Ariel University.
Papers
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Proceedings ArticleDOI
ITEM2VEC: Neural item embedding for collaborative filtering
Oren Barkan,Noam Koenigstein +1 more
TL;DR: Item2vec as mentioned in this paper is an item-based collaborative filtering method based on skip-gram with negative sampling (SGNS) that produces embedding for items in a latent space.
Proceedings ArticleDOI
Fast High Dimensional Vector Multiplication Face Recognition
TL;DR: This paper advances descriptor-based face recognition by suggesting a novel usage of descriptors to form an over-complete representation, and by proposing a new metric learning pipeline within the same/not-same framework.
Proceedings ArticleDOI
Evaluation of speech-based protocol for detection of early-stage dementia.
Aharon Satt,Alexander Sorin,Orith Toledo-Ronen,Oren Barkan,Ioannis Kompatsiaris,Athina Kokonozi,Magda Tsolaki +6 more
TL;DR: This analysis is based on recordings of over 80 diagnosed subjects; it yields dementia and MCI detection equal-error-rate below 20%, and demonstrates the high value of using speech and voice analysis for automatic screening and status tracking of dementia from the very early stage of MCI.
Proceedings Article
Bayesian Neural Word Embedding
TL;DR: The authors proposed a scalable Bayesian neural word embedding algorithm, which relies on a Variational Bayes solution for the Skip-Gram objective and a detailed step by step description is provided.
Proceedings ArticleDOI
CB2CF: a neural multiview content-to-collaborative filtering model for completely cold item recommendations
TL;DR: The CB2CF, a deep neural multiview model that serves as a bridge from items content into their CF representations, is presented, a "real-world" algorithm designed for Microsoft Store services that handle around a billion users worldwide.