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Title: Recommending personally interested contents by text mining, filtering, and interfaces

Abstract

A personalized content recommendation system includes a client interface device configured to monitor a user's information data stream. A collaborative filter remote from the client interface device generates automated predictions about the interests of the user. A database server stores personal behavioral profiles and user's preferences based on a plurality of monitored past behaviors and an output of the collaborative user personal interest inference engine. A programmed personal content recommendation server filters items in an incoming information stream with the personal behavioral profile and identifies only those items of the incoming information stream that substantially matches the personal behavioral profile. The identified personally relevant content is then recommended to the user following some priority that may consider the similarity between the personal interest matches, the context of the user information consumption behaviors that may be shown by the user's content consumption mode.

Inventors:
Issue Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1224433
Patent Number(s):
9171068
Application Number:
13/785,117
Assignee:
UT-Battelle, LLC (Oak Ridge, TN)
Patent Classifications (CPCs):
G - PHYSICS G06 - COMPUTING G06F - ELECTRIC DIGITAL DATA PROCESSING
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Patent
Resource Relation:
Patent File Date: 2013 Mar 05
Country of Publication:
United States
Language:
English
Subject:
96 KNOWLEDGE MANAGEMENT AND PRESERVATION

Citation Formats

Xu, Songhua. Recommending personally interested contents by text mining, filtering, and interfaces. United States: N. p., 2015. Web.
Xu, Songhua. Recommending personally interested contents by text mining, filtering, and interfaces. United States.
Xu, Songhua. Tue . "Recommending personally interested contents by text mining, filtering, and interfaces". United States. https://www.osti.gov/servlets/purl/1224433.
@article{osti_1224433,
title = {Recommending personally interested contents by text mining, filtering, and interfaces},
author = {Xu, Songhua},
abstractNote = {A personalized content recommendation system includes a client interface device configured to monitor a user's information data stream. A collaborative filter remote from the client interface device generates automated predictions about the interests of the user. A database server stores personal behavioral profiles and user's preferences based on a plurality of monitored past behaviors and an output of the collaborative user personal interest inference engine. A programmed personal content recommendation server filters items in an incoming information stream with the personal behavioral profile and identifies only those items of the incoming information stream that substantially matches the personal behavioral profile. The identified personally relevant content is then recommended to the user following some priority that may consider the similarity between the personal interest matches, the context of the user information consumption behaviors that may be shown by the user's content consumption mode.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2015},
month = {10}
}

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