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003 OCoLC;

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006 m o d ;

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

008 190827s2019 ncua obm 000 0 eng d;

035 a| (Sirsi) o1113942213;

035 a| (OCoLC)1113942213;

049 a| EREE;

090 a| ZA3084;

100 1 a| Maleki Shoja, Babak,e| author.?| UNAUTHORIZED;

245 10 a| Customer reviews analysis with deep neural networks for e-commerce recommender systems /c| by Babak Maleki Shoja.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2019.;

300 a| 69 pages :b| color illustrations;

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| PDFc| 3.456 MB2| rda;

538 a| System requirements: Adobe Reader.;

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

502 b| M.S.c| East Carolina Universityd| 2019.;

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 13, 2020).;

520 3 a| The first part of this thesis systematically reviews the trend of researches conducted from 2011 to 2018 in terms of challenges and problems regarding developing a recommendation system, areas of application, proposed methodologies, evaluations criteria used to assess the performance and limitations and drawbacks that require investigation and improvements. The study provides an overview for those who are interested in this field to understand the current and the future research opportunities. The second part of this thesis proposes a new methodology to consider customer reviews in recommender systems. An essential prerequisite of an effective recommender system is providing helpful information regarding users and items to generate high-quality recommendations. Customer reviews are a rich source of information that can offer insights into the recommender systems. However, dealing with the customer feedback in text format, as unstructured data, is challenging. Our research includes extraction of the features from customer reviews and use them for similarity evaluation of the users to generate the recommendations. To do so, we have developed a glossary of features for each product category using Latent Dirichlet Allocation. We then employed a deep neural network to extract deep features from the users-attributes matrix to deal with sparsity, ambiguity, and redundancy. Furthermore, we then applied matrix factorization as the collaborative filtering method to provide recommendations. The experimental results using Amazon dataset demonstrate that our methodology improves the performance of the recommender system by incorporating information from reviews when compared to the baselines.;

504 a| Includes bibliographical references.;

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

650 0 a| Marketing research.=| ^A17167;

650 0 a| Consumer satisfactionx| Analysis.=| ^A3793;

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

653 a| deep learning;

653 a| autoencoder;

653 a| customer review;

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/10342/7472;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 5168882;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5168882-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 8/27/2019x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5168882-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 8/27/2019x| ETDz| HERESOURCE;

Customer reviews analysis with deep neural networks for e-commerce recommender systems / by Babak Maleki Shoja.

Author/creator	Maleki Shoja, Babak 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], 2019.
Description	69 pages : color illustrations
Supplemental Content	Access via ScholarShip
Subjects	Recommender systems (Information filtering) Marketing research. Consumer satisfaction--Analysis. Machine learning.

Summary	The first part of this thesis systematically reviews the trend of researches conducted from 2011 to 2018 in terms of challenges and problems regarding developing a recommendation system, areas of application, proposed methodologies, evaluations criteria used to assess the performance and limitations and drawbacks that require investigation and improvements. The study provides an overview for those who are interested in this field to understand the current and the future research opportunities. The second part of this thesis proposes a new methodology to consider customer reviews in recommender systems. An essential prerequisite of an effective recommender system is providing helpful information regarding users and items to generate high-quality recommendations. Customer reviews are a rich source of information that can offer insights into the recommender systems. However, dealing with the customer feedback in text format, as unstructured data, is challenging. Our research includes extraction of the features from customer reviews and use them for similarity evaluation of the users to generate the recommendations. To do so, we have developed a glossary of features for each product category using Latent Dirichlet Allocation. We then employed a deep neural network to extract deep features from the users-attributes matrix to deal with sparsity, ambiguity, and redundancy. Furthermore, we then applied matrix factorization as the collaborative filtering method to provide recommendations. The experimental results using Amazon dataset demonstrate that our methodology improves the performance of the recommender system by incorporating information from reviews when compared to the baselines.
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 13, 2020).
Dissertation note	M.S. East Carolina University 2019.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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Customer reviews analysis with deep neural networks for e-commerce recommender systems / by Babak Maleki Shoja.

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