N
Noam Koenigstein
Researcher at Microsoft
Publications - 99
Citations - 2473
Noam Koenigstein is an academic researcher from Microsoft. The author has contributed to research in topics: Recommender system & Collaborative filtering. The author has an hindex of 21, co-authored 89 publications receiving 2100 citations. Previous affiliations of Noam Koenigstein include Tel Aviv University & Association for Computing Machinery.
Papers
More filters
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
Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy
TL;DR: A detailed analysis of a sparse, large scale dataset, specifically designed to push the envelope of recommender system models, including a rich bias model with terms that capture information from the taxonomy of items and different temporal dynamics of music ratings is described.
Proceedings Article
The Yahoo! Music Dataset and KDD-Cup'11
TL;DR: The organizers' account of the KDD-Cup 2011, which challenged the community to identify user tastes in music by leveraging Yahoo! Music user ratings, is provided, including a detailed analysis of the datasets, discussion of the contest goals and actual conduct, and lessons learned throughout the contest.
Proceedings ArticleDOI
Speeding up the Xbox recommender system using a euclidean transformation for inner-product spaces
Yoram Bachrach,Yehuda Finkelstein,Ran Gilad-Bachrach,Liran Katzir,Noam Koenigstein,Nir Nice,Ulrich Paquet +6 more
TL;DR: This work proposes a novel order preserving transformation, mapping the maximum inner product search problem to Euclidean space nearest neighbor search problem, and studies the efficiency of several (approximate) nearest neighbor data structures.
Proceedings ArticleDOI
One-class collaborative filtering with random graphs
Ulrich Paquet,Noam Koenigstein +1 more
TL;DR: This paper presents a novel Bayesian generative model for implicit collaborative filtering, which forms a core component of the Xbox Live architecture, and unlike previous approaches, delineates the odds of a user disliking an item from simply being unaware of it.