Tools

LEADER 03032cam a2200517Ii 4500

001 ocn968302872;

003 OCoLC;

005 20170412095613.0;

006 m o d ;

007 cr unu||||||||;

008 170113s2016 ncua obm 000 0 eng d;

035 a| (Sirsi) o968302872;

035 a| (OCoLC)968302872;

049 a| EREE;

090 a| QA76.9.I58;

100 1 a| Prateek, Prerna,e| author.?| UNAUTHORIZED;

245 10 a| Intelligent model for image-based recommendation system /c| by Prerna Prateek.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2016.;

300 a| 79 pages :b| illustrations (chiefly color);

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| PDFc| 6.34 MB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| M.S.c| East Carolina Universityd| 2016.;

500 a| Presented to the faculty of the Department of Computer Science.;

500 a| Advisor: Nasseh Tabrizi.;

500 a| Title from PDF t.p. (viewed January 27, 2017).;

520 3 a| Online shopping has developed in parallel with the Internet, and Recommendation Systems have played a pivotal role in its growth. The recommendations are usually provided in two ways: Content-based Filtering and Collaborative Filtering. Both forms of recommendations face the problem of Cold-Start due to an initial lack of information. To overcome this issue, Image-based Recommendation Systems are introduced in order to allow the users to locate products based on similarity of images when purchasing products in categories such as: clothes, shoes, home-decor, kitchen and dining utilities, jewelry, and accessories by mostly viewing images. In this thesis, a Hybrid Model of displaying similar images to that of the product being viewed was developed using Deep Features and Description-based Models. The Hybrid Model displayed a set composed of all images that belong to both Deep Features and Description-based Models. Implementation and comparison of results were performed on 100,000 images of SBU Captioned Photo Dataset.;

504 a| Includes bibliographical references.;

650 0 a| Recommender systems (Information filtering)=| ^A1260390;

650 0 a| Machine learning.=| ^A145211;

650 0 a| Teleshopping.=| ^A286684;

653 a| Image Retrieval;

653 a| Neural Network;

700 1 a| Tabrizi, M. H. N.,e| degree supervisor.?| UNAUTHORIZED;

710 2 a| East Carolina University.b| Department of Computer Science.?| UNAUTHORIZED;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/6004;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 4604922;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4604922-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 1/13/2017x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 4604922-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 1/13/2017x| ETDz| HERESOURCE;

Intelligent model for image-based recommendation system / by Prerna Prateek.

Author/creator	Prateek, Prerna author.
Other author	Tabrizi, M. H. N., degree supervisor.
Other author	East Carolina University. Department of Computer Science.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2016.
Description	79 pages : illustrations (chiefly color)
Supplemental Content	Access via ScholarShip
Subjects	Recommender systems (Information filtering) Machine learning. Teleshopping.

Summary	Online shopping has developed in parallel with the Internet, and Recommendation Systems have played a pivotal role in its growth. The recommendations are usually provided in two ways: Content-based Filtering and Collaborative Filtering. Both forms of recommendations face the problem of Cold-Start due to an initial lack of information. To overcome this issue, Image-based Recommendation Systems are introduced in order to allow the users to locate products based on similarity of images when purchasing products in categories such as: clothes, shoes, home-decor, kitchen and dining utilities, jewelry, and accessories by mostly viewing images. In this thesis, a Hybrid Model of displaying similar images to that of the product being viewed was developed using Deep Features and Description-based Models. The Hybrid Model displayed a set composed of all images that belong to both Deep Features and Description-based Models. Implementation and comparison of results were performed on 100,000 images of SBU Captioned Photo Dataset.
General note	Presented to the faculty of the Department of Computer Science.
General note	Advisor: Nasseh Tabrizi.
General note	Title from PDF t.p. (viewed January 27, 2017).
Dissertation note	M.S. East Carolina University 2016.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

Availability

Library	Location	Call Number	Status	Item Actions
	Electronic Resources	Access Content Online	✔ Available

Intelligent model for image-based recommendation system / by Prerna Prateek.

Click here for more information about this title

Availability