User Interface Design Recommendations Through Multi-Criteria Decision Analysis

Vairamuthu, S.; Amalanathan, Anthoniraj; Anouncia, S. Margret; Wiil, Uffe Kock

doi:10.1007/978-981-10-6680-1_14

Cited by 2 publications

(2 citation statements)

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“…e data sparsity affects negatively similarity information due to few or no co-rated items between two users [30]. As such, for improving the quality and the accuracy of the recommendations, MCDA is one of the most well-known practices with excellent results in this field [15,[27][28][29]. Using this method, the recommender system is able to represent more complex preferences of each user, since it takes into consideration multiple criteria for making its suggestions.…”

Section: Mcda-based Recommender Module: Architecture and Descriptionmentioning

confidence: 99%

“…Such problems may be overcome with the use of the MCDA method. Indeed, MCDA has been used in recommender systems for refining the suggestion of content to users [26][27][28], but to the best of our knowledge, it has not been applied yet sufficiently in digital repositories. In a review work of 2019 [29] about recommender systems for digital repositories, the author confirms that the main algorithms used by such systems are mainly the above ones, including content-based and collaborative filtering and hybrid methods.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Human-Computer Interaction in Digital Repositories through a MCDA-Based Recommender System

Troussas

Krouska

Sgouropoulou

2021

Advances in Human-Computer Interaction

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Digital repositories contain a large amount of content, which is available to heterogeneous groups of people. As such, in many cases people encounter difficulties in finding specific content which is related to their preferences. In view of this compelling need and towards advancing human-computer interaction, this paper presents a recommender system which is incorporated in a digital repository. The recommender system is designed using multiple-criteria decision analysis (MCDA) and more specifically the weighted sum model (WSM) in order to refine the delivered content to the users. It also considers several users’ characteristics (their preferences as depicted by the content they visited or searched and by the frequency of searches/visits) and features of the content (content types and traffic). The recommender system outputs the suggestions of content to users based on their preferences and interests. The presented recommender system was evaluated by real users, and the results show a high degree of accuracy in the recommended content and satisfaction by users.

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