PRUS: Product Recommender System Based on User Specifications and Customers Reviews
The rising popularity of online shopping has led to a steady stream of new product evaluations. Consumers benefit from these evaluations as they make purchasing decisions. Many research projects rank products using these reviews, however, most of these methodologies have ignored negative polarity wh...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10196427/ |
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| author | Naveed Hussain Hamid Turab Mirza Faiza Iqbal Ayesha Altaf Ahtsham Shoukat Monica Gracia Villar Emmanuel Soriano Flores Marco Antonio Rojo Gutierrez Imran Ashraf |
| author_facet | Naveed Hussain Hamid Turab Mirza Faiza Iqbal Ayesha Altaf Ahtsham Shoukat Monica Gracia Villar Emmanuel Soriano Flores Marco Antonio Rojo Gutierrez Imran Ashraf |
| author_sort | Naveed Hussain |
| collection | DOAJ |
| description | The rising popularity of online shopping has led to a steady stream of new product evaluations. Consumers benefit from these evaluations as they make purchasing decisions. Many research projects rank products using these reviews, however, most of these methodologies have ignored negative polarity while evaluating products for client needs. The main contribution of this research is the inclusion of negative polarity in the analysis of product rankings alongside positive polarity. To account for reviews that contain many sentiments and different elements, the suggested method first breaks them down into sentences. This process aids in determining the polarity of products at the phrase level by extracting elements from product evaluations. The next step is to link the polarity to the review’s sentence-level features. Products are prioritized following user needs by assigning relative importance to each of the polarities. The Amazon review dataset has been used in the experimental assessments so that the efficacy of the suggested approach can be estimated. Experimental evaluation of PRUS utilizes rank score (<inline-formula> <tex-math notation="LaTeX">$RS$ </tex-math></inline-formula>) and normalized discounted cumulative gain (<inline-formula> <tex-math notation="LaTeX">$nDCG$ </tex-math></inline-formula>) score. Results indicate that PRUS gives independence to the user to select recommended list based on specific features with respect to positive or negative aspects of the products. |
| first_indexed | 2024-03-12T16:22:50Z |
| format | Article |
| id | doaj.art-cb1472284d0447b782dbdf801c513d46 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T16:22:50Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cb1472284d0447b782dbdf801c513d462023-08-08T23:00:37ZengIEEEIEEE Access2169-35362023-01-0111812898129710.1109/ACCESS.2023.329981810196427PRUS: Product Recommender System Based on User Specifications and Customers ReviewsNaveed Hussain0https://orcid.org/0000-0003-3874-2088Hamid Turab Mirza1https://orcid.org/0000-0002-2280-2704Faiza Iqbal2Ayesha Altaf3https://orcid.org/0000-0001-6446-4945Ahtsham Shoukat4https://orcid.org/0000-0002-8456-2451Monica Gracia Villar5Emmanuel Soriano Flores6Marco Antonio Rojo Gutierrez7Imran Ashraf8https://orcid.org/0000-0002-8271-6496Faculty of Information Technology and Computer Science, University of Central Punjab, Lahore, PakistanDepartment of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, PakistanDepartment of Computer Science, University of Engineering and Technology Lahore, Lahore, PakistanDepartment of Computer Science, University of Engineering and Technology Lahore, Lahore, PakistanDepartment of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, PakistanFaculty of Social Science and Humanities, Universidad Europea del Atlántico, Santander, SpainFaculty of Social Science and Humanities, Universidad Europea del Atlántico, Santander, SpainFaculty of Social Science and Humanities, Universidad Europea del Atlántico, Santander, SpainDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, Republic of KoreaThe rising popularity of online shopping has led to a steady stream of new product evaluations. Consumers benefit from these evaluations as they make purchasing decisions. Many research projects rank products using these reviews, however, most of these methodologies have ignored negative polarity while evaluating products for client needs. The main contribution of this research is the inclusion of negative polarity in the analysis of product rankings alongside positive polarity. To account for reviews that contain many sentiments and different elements, the suggested method first breaks them down into sentences. This process aids in determining the polarity of products at the phrase level by extracting elements from product evaluations. The next step is to link the polarity to the review’s sentence-level features. Products are prioritized following user needs by assigning relative importance to each of the polarities. The Amazon review dataset has been used in the experimental assessments so that the efficacy of the suggested approach can be estimated. Experimental evaluation of PRUS utilizes rank score (<inline-formula> <tex-math notation="LaTeX">$RS$ </tex-math></inline-formula>) and normalized discounted cumulative gain (<inline-formula> <tex-math notation="LaTeX">$nDCG$ </tex-math></inline-formula>) score. Results indicate that PRUS gives independence to the user to select recommended list based on specific features with respect to positive or negative aspects of the products.https://ieeexplore.ieee.org/document/10196427/Customers reviewsproduct rankingsentiment analysisuser specificationfeature extraction |
| spellingShingle | Naveed Hussain Hamid Turab Mirza Faiza Iqbal Ayesha Altaf Ahtsham Shoukat Monica Gracia Villar Emmanuel Soriano Flores Marco Antonio Rojo Gutierrez Imran Ashraf PRUS: Product Recommender System Based on User Specifications and Customers Reviews IEEE Access Customers reviews product ranking sentiment analysis user specification feature extraction |
| title | PRUS: Product Recommender System Based on User Specifications and Customers Reviews |
| title_full | PRUS: Product Recommender System Based on User Specifications and Customers Reviews |
| title_fullStr | PRUS: Product Recommender System Based on User Specifications and Customers Reviews |
| title_full_unstemmed | PRUS: Product Recommender System Based on User Specifications and Customers Reviews |
| title_short | PRUS: Product Recommender System Based on User Specifications and Customers Reviews |
| title_sort | prus product recommender system based on user specifications and customers reviews |
| topic | Customers reviews product ranking sentiment analysis user specification feature extraction |
| url | https://ieeexplore.ieee.org/document/10196427/ |
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