A Hybrid Recommender System for HCI Design Pattern Recommendations
User interface design patterns are acknowledged as a standard solution to recurring design problems. The heterogeneity of existing design patterns makes the selection of relevant ones difficult. To tackle these concerns, the current work contributes in a twofold manner. The first contribution is the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-11-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/22/10776 |
| _version_ | 1797511366564642816 |
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| author | Amani Braham Maha Khemaja Félix Buendía Faiez Gargouri |
| author_facet | Amani Braham Maha Khemaja Félix Buendía Faiez Gargouri |
| author_sort | Amani Braham |
| collection | DOAJ |
| description | User interface design patterns are acknowledged as a standard solution to recurring design problems. The heterogeneity of existing design patterns makes the selection of relevant ones difficult. To tackle these concerns, the current work contributes in a twofold manner. The first contribution is the development of a recommender system for selecting the most relevant design patterns in the Human Computer Interaction (HCI) domain. This system introduces a hybrid approach that combines text-based and ontology-based techniques and is aimed at using semantic similarity along with ontology models to retrieve appropriate HCI design patterns. The second contribution addresses the validation of the proposed recommender system regarding the acceptance intention towards our system by assessing the perceived experience and the perceived accuracy. To this purpose, we conducted a user-centric evaluation experiment wherein participants were invited to fill pre-study and post-test questionnaires. The findings of the evaluation study revealed that the perceived experience of the proposed system’s quality and the accuracy of the recommended design patterns were assessed positively. |
| first_indexed | 2024-03-10T05:44:19Z |
| format | Article |
| id | doaj.art-93ffbb0e7aad4b85aca710d7e8240251 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T05:44:19Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-93ffbb0e7aad4b85aca710d7e82402512023-11-22T22:18:43ZengMDPI AGApplied Sciences2076-34172021-11-0111221077610.3390/app112210776A Hybrid Recommender System for HCI Design Pattern RecommendationsAmani Braham0Maha Khemaja1Félix Buendía2Faiez Gargouri3Department of Computer Engineering, Universitat Politècnica de Valencia, Camino de Vera S/N, 46022 Valencia, SpainPRINCE Research Lab, ISITCOM, University of Sousse, Sousse 4011, TunisiaEscuela Técnica Superior de Informática (ETSINF), Universitat Politècnica de Valencia, Camino de Vera S/N, 46022 Valencia, SpainMIRACL Laboratory, University of Sfax, Sfax 3029, TunisiaUser interface design patterns are acknowledged as a standard solution to recurring design problems. The heterogeneity of existing design patterns makes the selection of relevant ones difficult. To tackle these concerns, the current work contributes in a twofold manner. The first contribution is the development of a recommender system for selecting the most relevant design patterns in the Human Computer Interaction (HCI) domain. This system introduces a hybrid approach that combines text-based and ontology-based techniques and is aimed at using semantic similarity along with ontology models to retrieve appropriate HCI design patterns. The second contribution addresses the validation of the proposed recommender system regarding the acceptance intention towards our system by assessing the perceived experience and the perceived accuracy. To this purpose, we conducted a user-centric evaluation experiment wherein participants were invited to fill pre-study and post-test questionnaires. The findings of the evaluation study revealed that the perceived experience of the proposed system’s quality and the accuracy of the recommended design patterns were assessed positively.https://www.mdpi.com/2076-3417/11/22/10776HCIdesign patternsdesign problemssemantic similarityontology modelsrecommender system |
| spellingShingle | Amani Braham Maha Khemaja Félix Buendía Faiez Gargouri A Hybrid Recommender System for HCI Design Pattern Recommendations Applied Sciences HCI design patterns design problems semantic similarity ontology models recommender system |
| title | A Hybrid Recommender System for HCI Design Pattern Recommendations |
| title_full | A Hybrid Recommender System for HCI Design Pattern Recommendations |
| title_fullStr | A Hybrid Recommender System for HCI Design Pattern Recommendations |
| title_full_unstemmed | A Hybrid Recommender System for HCI Design Pattern Recommendations |
| title_short | A Hybrid Recommender System for HCI Design Pattern Recommendations |
| title_sort | hybrid recommender system for hci design pattern recommendations |
| topic | HCI design patterns design problems semantic similarity ontology models recommender system |
| url | https://www.mdpi.com/2076-3417/11/22/10776 |
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