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Noam Koenigstein patents

Recent patents with Noam Koenigstein listed as an inventor - additional entries may be under other spellings.


Noam Koenigstein - Related organizations: Microsoft Technology Licensing patents, Microsoft Corporation patents

Multilingual content based recommendation system

05/19/16 - 20160140643 - Example apparatus and methods access multiple sources of information concerning features for applications, clean the data from the multiple sources, extract features from the cleaned data, selectively weight the sources, data or extracted features and produce a feature vector. The feature vector may then be used in a single language
Inventors: Nir Nice, Noam Koenigstein, Shay Ben-elazar, Shahar Keren, Ulrich Paquet, Yehuda Finkelstein

Hybrid explanations in collaborative filter based recommendation system

05/12/16 - 20160132601 - Example apparatus and methods produce an explanation of why a recommendation is being made by an automated collaborative filtering recommendation system. The explanation may include feature categories and features that describe the item being recommended. The feature categories selected and features selected may depend on a personalization level for an
Inventors: Nir Nice, Noam Koenigstein, Shay Ben-elazar

Modified matrix factorization of content-based model for recommendation system

03/17/16 - 20160078520 - A recommendation system is implemented using modified matrix factorization on top of a content-based matrix to provide both user-to-item and item-to-item content-based recommendations while exposing the full depth of transitive relationships among recommendations. Content information such as features and characteristics may be represented in a usage matrix in which features
Inventors: Nir Nice, Noam Koenigstein, Shahar Keren, Ayelet Kroskin, Ulrich Paquet

Directed recommendations

10/01/15 - 20150278910 - Example apparatus and methods perform matrix factorization (MF) on a usage matrix to create a latent space that describes similarities between items in the usage matrix. The usage matrix relates source items that a user already has to target items that a user might acquire. A cell in the usage
Inventors: Nir Nice, Noam Koenigstein, Ulrich Paquet, Yehuda Finkelstein

Recommendation system with multi-dimensional discovery experience

10/01/15 - 20150278908 - Example apparatus and methods perform matrix factorization (MF) on a collaborative filter based usage matrix to create a multi-dimensional latent space that embeds users, items, and features. A full distance matrix is extracted from the latent space. The full distance matrix may be extracted from the latent space by defining
Inventors: Nir Nice, Noam Koenigstein, Ulrich Paquet, Shahar Keren, Daniel Sitton, Amit Perelstein

User inactivity aware recommendation system

10/01/15 - 20150278907 - Example apparatus and methods perform matrix factorization (MF) on a usage matrix to create a latent space that describes similarities between users and items in the usage matrix. The usage matrix relates users to items according to a collaborative filtering approach. A cell in the usage matrix may store a
Inventors: Nir Nice, Noam Koenigstein, Ulrich Paquet, Shahar Keren

Recommendation system with dual collaborative filter usage matrix

10/01/15 - 20150278350 - Example apparatus and methods perform matrix factorization (MF) on a usage matrix to create a latent space that describes similarities between users and items and between items and items in the usage matrix. The usage matrix relates users to items according to a collaborative filtering approach. A cell in the
Inventors: Nir Nice, Noam Koenigstein, Ulrich Paquet, Shahar Keren

Multiple persona based modeling

04/23/15 - 20150112801 - Matrix factorization techniques may be employed to identify different tastes based on user history information for a user profile and to provide item recommendations for the various tastes. An item model may be generated that includes item vectors, each item vector representing an item from a catalog of items. An
Inventors: Nir Nice, Noam Koenigstein, Ulrich Paquet, Shahar Zvi Keren

Feature selection for recommender systems

03/12/15 - 20150073931 - Disclosed herein is a system and method for identifying features of items that are more relevant for making recommendations to consumers for content that they may be interested in. The system determines the similarity between items that are recommend and items in the user's history and compares that similarity measure
Inventors: Royi Ronen, Noam Koenigstein, Nir Nice, Elad Ziklik

Automatic audience detection for modifying user profiles and making group recommendations

12/18/14 - 20140372430 - Disclosed herein is a system and method for determining that a current user profile in a system should be modified or changed. An audience detection component detects that a characteristic has been detected that does not match at least one characteristic in the current user profile. The audience detection component
Inventors: Mikael Sitruk, Noam Koenigstein, Royi Ronen, Elad Ziklik

Feature embedding in matrix factorization

06/26/14 - 20140181121 - In various embodiments, systems and methods are provided for enhancing media content recommendations by using feature vectors. An enhanced-matrix having a first portion and a second portion is received. The first portion of the enhanced-matrix includes a user-item matrix and the second portion of the enhanced-matrix includes a feature-item matrix.
Inventors: Nir Nice, Noam Koenigstein, Ulrich Paquet, Shahar Zvi Keren, Andrew Jaffray

Efficient modeling system

05/08/14 - 20140129500 - A technique for efficiently factoring a matrix in a recommendation system. Usage data for a large set of users relative to a set of items is provided in a usage matrix R. To reduce computational requirements, the usage matrix is sampled to provide a reduced matrix R′. R′ is factored
Inventors: Nir Nice, Noam Koenigstein, Ulrich Paquet, Shahar Keren, Daniel Sitton, Dror Kremer, Shai Roitman

Recommender system

08/22/13 - 20130218907 - Embodiments of the invention provide methods and apparatus for recommending items from a catalog of items to users in a population of users by generating trait vectors that represent items in the catalog responsive to explicit and/or implicit preference data for a group of less than all the users and
Inventors: Nir Nice, Shahar Keren, Ori Folger, Ulrich Paquet, Shimon Shlevich, Noam Koenigstein, Eylon Yogev

Recommender system

08/15/13 - 20130211950 - Embodiments of the invention provide methods and apparatus for recommending items from a catalog of items to a user by parsing the catalog of items into a plurality of catalog clusters of related items and recommending catalog items to the user from catalog clusters to which items previously preferred by
Inventors: Nir Nice, Noam Koenigstein, Ulrich Paquet

Method, a system and a computer program product for detecting a local phenomenon

11/12/09 - 20090282029 - A system for detecting a local phenomenon, the system includes an interface for receiving queries information from a system for retrieving art related media, and a processor, configured to: (a) create a first local popularity chart, wherein the creating of the first local popularity chart includes enumerating, for each geographic
Inventors: Noam Koenigstein, Yuval Shavitt, Tomer Tankel


### Noam Koenigstein patent invention listings

The bibliographic references displayed about Noam Koenigstein's patents are for a recent sample of Noam Koenigstein's publicly published patent applications. The inventor/author may have additional bibliographic citations listed at the USPTO.gov. FreshPatents.com is not associated or affiliated in any way with the author/inventor or the United States Patent/Trademark Office but is providing this non-comprehensive sample listing for educational and research purposes using public bibliographic data published and disseminated from the United States Patent/Trademark Office public datafeed. This information is also available for free on the USPTO.gov website. If Noam Koenigstein filed recent patent applications under another name, spelling or location then those applications could be listed on an alternate page. If no bibliographic references are listed here, it is possible there are no recent filings or there is a technical issue with the listing--in that case, we recommend doing a search on the USPTO.gov website.

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