eprintid: 8635 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/00/86/35 datestamp: 2023-09-04 23:30:06 lastmod: 2023-09-04 23:30:08 status_changed: 2023-09-04 23:30:06 type: article metadata_visibility: show creators_name: Hussain, Naveed creators_name: Mirza, Hamid Turab creators_name: Iqbal, Faiza creators_name: Altaf, Ayesha creators_name: Shoukat, Ahtsham creators_name: Gracia Villar, Mónica creators_name: Soriano Flores, Emmanuel creators_name: Rojo Gutiérrez, Marco Antonio creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: monica.gracia@uneatlantico.es creators_id: emmanuel.soriano@uneatlantico.es creators_id: marco.rojo@unini.edu.mx creators_id: title: PRUS: Product Recommender System Based on User Specifications and Customers Reviews ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: unincol_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: Customers reviews; product ranking; sentiment analysis; user specification; feature extraction abstract: 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 ( RS ) and normalized discounted cumulative gain ( nDCG ) 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. date: 2023-07 publication: IEEE Access volume: 11 pagerange: 81289-81297 id_number: doi:10.1109/ACCESS.2023.3299818 refereed: TRUE issn: 2169-3536 official_url: http://doi.org/10.1109/ACCESS.2023.3299818 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés 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 ( RS ) and normalized discounted cumulative gain ( nDCG ) 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. metadata Hussain, Naveed; Mirza, Hamid Turab; Iqbal, Faiza; Altaf, Ayesha; Shoukat, Ahtsham; Gracia Villar, Mónica; Soriano Flores, Emmanuel; Rojo Gutiérrez, Marco Antonio y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, monica.gracia@uneatlantico.es, emmanuel.soriano@uneatlantico.es, marco.rojo@unini.edu.mx, SIN ESPECIFICAR (2023) PRUS: Product Recommender System Based on User Specifications and Customers Reviews. IEEE Access, 11. pp. 81289-81297. ISSN 2169-3536 document_url: http://repositorio.unib.org/id/eprint/8635/1/PRUS_Product_Recommender_System_Based_on_User_Specifications_and_Customers_Reviews.pdf