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Title: System and method of content based recommendation using hypernym expansion

Abstract

A system and method for document recommendation combines linguistic and term frequency approaches in order to improve overall performance and recommendation. Hypernyms are added to the source document set so that related documents can be recommended from the source document set. There are a variety of different parameters to configure that change operation of the document recommendation system.

Inventors:
; ;
Issue Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1650864
Patent Number(s):
10643031
Application Number:
15/455,260
Assignee:
UT-Battelle, LLC (Oak Ridge, TN)
Patent Classifications (CPCs):
G - PHYSICS G16 - INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS G16H - HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
G - PHYSICS G06 - COMPUTING G06F - ELECTRIC DIGITAL DATA PROCESSING
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Patent
Resource Relation:
Patent File Date: 03/10/2017
Country of Publication:
United States
Language:
English

Citation Formats

Potok, Thomas E., Patton, Robert M., and Sukumar, Sreenivas R.. System and method of content based recommendation using hypernym expansion. United States: N. p., 2020. Web.
Potok, Thomas E., Patton, Robert M., & Sukumar, Sreenivas R.. System and method of content based recommendation using hypernym expansion. United States.
Potok, Thomas E., Patton, Robert M., and Sukumar, Sreenivas R.. Tue . "System and method of content based recommendation using hypernym expansion". United States. https://www.osti.gov/servlets/purl/1650864.
@article{osti_1650864,
title = {System and method of content based recommendation using hypernym expansion},
author = {Potok, Thomas E. and Patton, Robert M. and Sukumar, Sreenivas R.},
abstractNote = {A system and method for document recommendation combines linguistic and term frequency approaches in order to improve overall performance and recommendation. Hypernyms are added to the source document set so that related documents can be recommended from the source document set. There are a variety of different parameters to configure that change operation of the document recommendation system.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {5}
}

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