Development of web based data-driven recommendation system for house rental via hierarchical fuzzy axiomatic design.
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| Title: | Development of web based data-driven recommendation system for house rental via hierarchical fuzzy axiomatic design. |
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| Authors: | Şenyüzlüler, Filiz1 (AUTHOR) filissen@gmail.com, Baykasoglu, Adil1 (AUTHOR) adil.baykasoglu@deu.edu.tr |
| Source: | Kybernetes. 2026, Vol. 55 Issue 2, p1156-1182. 27p. |
| Subjects: | Recommender systems, Rental housing, Consumer preferences, Web-based user interfaces, Quantitative research, Real property, Metadata, Fuzzy decision making |
| Abstract: | Purpose: In the real estate business, identifying the ideal property for a user poses a difficult task due to the many factors involved in the decision-making process. Moreover, users often struggle to find platforms that facilitate effective communication of their preferences. To tackle this issue, a web-based data-driven recommendation system has been devised for the real estate business. Design/methodology/approach: The process of identifying the most suitable rental property for a user hinges greatly on how the user prioritizes each criterion and the analysis of unstructured data. In this research, a novel recommendation system for house rentals is developed by utilizing the Weighted Hierarchical Fuzzy Axiomatic Design (WFAD) approach. Techniques for extracting pertinent information from unstructured house descriptions are employed. The user's preferences are captured through an interactive web application equipped with a map feature to highlight key locations. Findings: Data on various available rental properties are gathered using web scraping techniques. The efficacy of the proposed rental house recommendation system is demonstrated through multiple case studies. It is observed that the developed system provides more informed and reliable decisions. Originality/value: First time in the related literature, we applied the weighted fuzzy axiomatic design procedure (WFAD) to the product recommendation problem and developed a comprehensive web-based system for recommending rental houses based on it in the real estate business. [ABSTRACT FROM AUTHOR] |
| Copyright of Kybernetes is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 191147871 AccessLevel: 6 PubType: Periodical PubTypeId: serialPeriodical PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Development of web based data-driven recommendation system for house rental via hierarchical fuzzy axiomatic design. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Şenyüzlüler%2C+Filiz%22">Şenyüzlüler, Filiz</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> filissen@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Baykasoglu%2C+Adil%22">Baykasoglu, Adil</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> adil.baykasoglu@deu.edu.tr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Kybernetes%22">Kybernetes</searchLink>. 2026, Vol. 55 Issue 2, p1156-1182. 27p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Recommender+systems%22">Recommender systems</searchLink><br /><searchLink fieldCode="DE" term="%22Rental+housing%22">Rental housing</searchLink><br /><searchLink fieldCode="DE" term="%22Consumer+preferences%22">Consumer preferences</searchLink><br /><searchLink fieldCode="DE" term="%22Web-based+user+interfaces%22">Web-based user interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22Quantitative+research%22">Quantitative research</searchLink><br /><searchLink fieldCode="DE" term="%22Real+property%22">Real property</searchLink><br /><searchLink fieldCode="DE" term="%22Metadata%22">Metadata</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+decision+making%22">Fuzzy decision making</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: In the real estate business, identifying the ideal property for a user poses a difficult task due to the many factors involved in the decision-making process. Moreover, users often struggle to find platforms that facilitate effective communication of their preferences. To tackle this issue, a web-based data-driven recommendation system has been devised for the real estate business. Design/methodology/approach: The process of identifying the most suitable rental property for a user hinges greatly on how the user prioritizes each criterion and the analysis of unstructured data. In this research, a novel recommendation system for house rentals is developed by utilizing the Weighted Hierarchical Fuzzy Axiomatic Design (WFAD) approach. Techniques for extracting pertinent information from unstructured house descriptions are employed. The user's preferences are captured through an interactive web application equipped with a map feature to highlight key locations. Findings: Data on various available rental properties are gathered using web scraping techniques. The efficacy of the proposed rental house recommendation system is demonstrated through multiple case studies. It is observed that the developed system provides more informed and reliable decisions. Originality/value: First time in the related literature, we applied the weighted fuzzy axiomatic design procedure (WFAD) to the product recommendation problem and developed a comprehensive web-based system for recommending rental houses based on it in the real estate business. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Kybernetes is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 1156 Subjects: – SubjectFull: Recommender systems Type: general – SubjectFull: Rental housing Type: general – SubjectFull: Consumer preferences Type: general – SubjectFull: Web-based user interfaces Type: general – SubjectFull: Quantitative research Type: general – SubjectFull: Real property Type: general – SubjectFull: Metadata Type: general – SubjectFull: Fuzzy decision making Type: general Titles: – TitleFull: Development of web based data-driven recommendation system for house rental via hierarchical fuzzy axiomatic design. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Şenyüzlüler, Filiz – PersonEntity: Name: NameFull: Baykasoglu, Adil IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 01 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 0368492X Numbering: – Type: volume Value: 55 – Type: issue Value: 2 Titles: – TitleFull: Kybernetes Type: main |
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