A Hybrid Recommender System for HCI Design Pattern Recommendations

[EN] User interface design patterns are acknowledged as a standard solution to recurring de¿ sign 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 con¿ tribution is the development of a recommender system for selecting the most relevant design pat¿ terns 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 pur¿ pose, 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 per¿ ceived experience of the proposed system¿s quality and the accuracy of the recommended design patterns were assessed positively. Braham, A.; Khemaja, M.; Buendía García, F.; Gargouri, F. (2021). A Hybrid Recommender System for HCI Design Pattern Recommendations. Applied Sciences. 11(22):1-25. https://doi.org/10.3390/app112210776