# A Hybrid Recommender System for HCI Design Pattern Recommendations
###### Abstract  
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. HCI; design patterns; design problems; semantic similarity; ontology models; recommender system +
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The cornerstone of the design pattern concept was laid down in the architecture domain by [PERSON] [4]. This concept was initially meant to focus on frequent problems faced by designers in order to offer a correct solution within a particular context. The design pattern concept was later transferred to software design when [PERSON] et al. [5] introduced design patterns as a way to share the design solutions of experienced developers. After that, design patterns emerged in the HCI domain to capture HCI knowledge [6]. They have sparked interest in various areas, including UI and Web design [7; 8; 9]. In this sense, developers can take advantage of freely reusing existing design knowledge to elaborate efficient and adaptive UIs and save on development time. In recent decades, an increasing number of design patterns in the HCI domain have been noticed. Moreover, several design pattern repositories and catalogues have been organized and published [10; 11; 12]. The sheer amount of available design patterns offers good design solutions to recurring design problems. Nevertheless, it is difficult for developers or designers to follow all available HCI design patterns during the development of UIs and to select the right design patterns when a design problem is tackled. This is especially true when design patterns are stored within more than one repository. To overcome these issues, a supporting system that recommends appropriate design patterns is required. Recommender systems have become an emerging research area where information overload is a major problem. In general, recommender systems are filtering systems [13] that handle the problem of information overload by retrieving the most relevant elements and services from a large amount of data. In recent decades, various approaches have been proposed for developing recommender systems. In this sense, there are a significant number of studies that introduce different recommendation approaches for selecting relevant design patterns, including text classification [14], case-based reasoning [15], and ontology-based [16] approaches. Fortunately, various recommender systems consider these approaches to (semi-)automatically select appropriate design patterns. Nevertheless, design patterns in the HCI domain have not been well-adopted within existing systems. In this work, we propose a different approach to address the challenge of exploring design pattern recommendations in the HCI domain. The main contribution of the present approach is twofold. The first contribution of this paper relates to the development of a recommender system for selecting the most relevant HCI design patterns for a given design problem.