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A Data Driven Recommender Framework.

Rutgers The State University of New Jersey, School of Graduate Studies.

A Data Driven Recommender Framework.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	A Data Driven Recommender Framework./
Author:	Varshney, Shubhank.
Description:	1 online resource (37 pages)
Notes:	Source: Masters Abstracts International, Volume: 57-05.
Contained By:	Masters Abstracts International57-05(E).
Subject:	Electrical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780355779318

A Data Driven Recommender Framework.
Varshney, Shubhank.

A Data Driven Recommender Framework. - 1 online resource (37 pages)

Source: Masters Abstracts International, Volume: 57-05.

Thesis (M.S.)--Rutgers The State University of New Jersey, School of Graduate Studies, 2017.

Includes bibliographical references

Process mining has been receiving a large amount of scrutiny by researches and industry personnel alike in the recent past. The process logs and traces left by business process can be a big source of information and knowledge about the behavioral aspects of the process. Process mining techniques help extract the knowledge from the traces and logs. This knowledge based data coupled with process mining can used to design recommendations for a user. Most of the existing recommender systems have not been developed based on process mining algorithms and hence provide a whole new scope for research and improvement in their development. Therefore, in this thesis we propose a novel data driven recommender model that can provide recommendations of sequential procedural steps, visualizations and diagnostics for a specific group of patients to provide a feasible and more accurate solution to the problem.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355779318Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

A Data Driven Recommender Framework.
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035 $a (MiAaPQ)AAI10799439
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100 1 $a Varshney, Shubhank. $3 1194267
245 1 2 $a A Data Driven Recommender Framework.
264 0 $c 2017
300 $a 1 online resource (37 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 57-05.
500 $a Adviser: Ivan Marsic.
502 $a Thesis (M.S.)--Rutgers The State University of New Jersey, School of Graduate Studies, 2017.
504 $a Includes bibliographical references
520 $a Process mining has been receiving a large amount of scrutiny by researches and industry personnel alike in the recent past. The process logs and traces left by business process can be a big source of information and knowledge about the behavioral aspects of the process. Process mining techniques help extract the knowledge from the traces and logs. This knowledge based data coupled with process mining can used to design recommendations for a user. Most of the existing recommender systems have not been developed based on process mining algorithms and hence provide a whole new scope for research and improvement in their development. Therefore, in this thesis we propose a novel data driven recommender model that can provide recommendations of sequential procedural steps, visualizations and diagnostics for a specific group of patients to provide a feasible and more accurate solution to the problem.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Rutgers The State University of New Jersey, School of Graduate Studies. $b School of Graduate Studies. $3 1193059
773 0 $t Masters Abstracts International $g 57-05(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10799439 $z click for full text (PQDT)

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