A Survey of Data Mining Techniques for Smart Museum Applications

This research aims to find out what data mining techniques are effectively implemented in museums and what application trends are currently being used to improve museum performance towards modern museums based on intelligent system technology. The review was carried out on a number of articles found in journals and proceedings in the 2004-2020 period. It is found that the majority of data mining techniques are implemented in museum virtual guide applications, recommender systems, collection clustering and classification system, and   visitor behaviour prediction application. Data classification, clustering, and prediction technique commonly used for museum application.  Collections with historical and artistic value  contain a lot of knowledge making data mining an important technique to be included in various applications in museums so that they can have an impact on the achievement of museum goals not only in the fields of education and culture but also economics and business

Rancang Bangun Sistem Rekomendasi Peminatan Fakultas Teknologi Informasi Dan Komunikasi Dengan Metode Analytical Hierarchy Process

. The tight competition in these globalization era encourages university students to pick suitable specialization courses while studying in university. University students will not only gain the knowledge from those particular specialization courses but they will also get the practical skills and the certification, as well as improved talents. Students have to consult to one of the lecturers for guidance to pick the most suitable courses for the students. However this method is time consuming. For such reason, the main purpose of this research is to build a recommendation system that utilizes Analytical Hierarchy Process to allow students to decide the course specialization. This system is be built based on Android and PHP programming language to process the recommendation calculation. According to the questionnaire done, more than 50% of the respondents confirm that this application has good accuracy rate, and as much as 40% respondents confirm that the application is able to provide course specialization recommendation that meets their preferences

Personalityzation: UI Personalization, Theoretical Grounding in HCI and Design Research

Personalization is an effective means for accommodating differences between individuals. Therefore, the personalization of a system’s user interface (UI) features can enhance usability. To date, UI personalization approaches have been largely divorced from psychological theories of personality, and the user profiles constructed by extant personalization techniques do not map directly onto the fundamental personality traits examined in the psychology literature. In line with recent calls to ground the design of information systems in behavioral theory, we maintain that personalization that is informed by psychology literature is advantageous. More specifically, we advocate an approach termed “personalityzation”, where UI features are adapted to an explicit model of a user’s personality. We demonstrate the proposed personalityzation approach through a proof-of-concept in the context of social recommender systems. We identify two key contributions to information systems research. First, extending prior works on adaptive interfaces, we introduce a UI personalization framework that is grounded in psychology theory of personality. Second, we reflect on how our proposed personalityzation framework could inform the discourse in design research regarding the theoretical grounding of system’s design

Transforming visitor experience with museum technologies: The development and impact evaluation of a recommender system in a physical museum

Over the past few decades, many attempts have been made to develop recommender systems (RSs) that could improve visitor experience (VX) in physical museums. Nevertheless, to determine the effectiveness of a museum RS, studies often encompass system performance evaluations, e.g., user experience (UX) and accuracy level tests, and rarely extend to the VX realm that museum RSs aim to support. The reported challenges with defining and evaluating VX might explain why the evidence that the interaction with an RS during the visit can enhance the quality of VX remains limited. Without this evidence, however, the purpose of developing museum RSs and the benefits of using RSs during a museum visit are in question. This thesis interrogates whether and how museum RSs can impact VX. It first consolidates the literature about VX-related constructs into one coherent analytical framework of museum experience which delineates the scope of VX. Following this analysis, this research develops and validates a VX instrument with cognitive, introspective, restorative, and affective variables which could be used to evaluate VX with or without museum technologies. Then, through a series of UX- and VX-related studies in the physical museum, this research implements a fully working content-based RS and establishes how the interaction with the developed RS transforms VX. The findings in this thesis demonstrate that the impact of an RS on the quality of VX can depend on the level of engagement with the system during a museum visit. Additionally, the impact can be insufficient on some mental processes within VX, and it can vary following the changes in contextual variables. The findings also reinforce that system performance tests cannot replace a VX-focused analysis, because a positive UX and additional information about museum objects in an RS do not imply an improved VX. Therefore, this thesis underscores that more VX-related evaluations of museum RSs are required to identify how to strengthen and extend their influence on the quality of VX

VR Technologies in Cultural Heritage

This open access book constitutes the refereed proceedings of the First International Conference on VR Technologies in Cultural Heritage, VRTCH 2018, held in Brasov, Romania in May 2018. The 13 revised full papers along with the 5 short papers presented were carefully reviewed and selected from 21 submissions. The papers of this volume are organized in topical sections on data acquisition and modelling, visualization methods / audio, sensors and actuators, data management, restoration and digitization, cultural tourism

Customers’ loyalty model in the design of e-commerce recommender systems

Recommender systems have been adopted in most modern online platforms to guide users in finding more suitable items that match their interests. Previous studies showed that recommender systems impact the buying behavior of e-commerce customers. However, service providers are more concerned about the continuing behavior of their customers, specifically customers’ loyalty, which is an important factor to increase service providers’ share of wallet. Therefore, this study aimed to investigate the customers’ loyalty factors in online shopping towards e-commerce recommender systems. To address the research objectives, a new research model was proposed based on the Cognition-Affect-Behavior model. To validate the research model, a quantitative methodology was utilized to gather the relevant data. Using a survey method, a total of 310 responses were gathered to examine the impacts of the identified factors on customers’ loyalty towards Amazon’s recommender system. Data was analysed using Partial Least Square Structural Equation Modelling. The results of the analysis indicated that Usability (P=0.467, t=5.139, p<0.001), Service Interaction (P=0.304, t=4.42, p<0.001), Website Quality (P=0.625, t=15.304, p<0.001), Accuracy (P=0.397, t=6.144, p<0.001), Novelty (P=0.289, t=4.406, p<0.001), Diversity (P=0.142, t=2.503, p<0.001), Recommendation Quality (P=0.423, t=7.719, p<0.001), Explanation (P=0.629, t=15.408, p<0.001), Transparency (P=0.279, t=5.859, p<0.001), Satisfaction (P=0.152, t=3.045, p<0.001) and Trust (P=0.706, t=14.14, p<0.001) have significant impacts on customers’ loyalty towards the recommender systems in online shopping. Information quality, however, did not affect the quality of the website that hosted the recommender system. The findings demonstrated that accuracy-oriented measures were insufficient in understanding customer behavior, and other quality factors, such as diversity, novelty, and transparency could improve customers’ loyalty towards recommender systems. The outcomes of the study indicated the significant impact of the website quality on customers’ loyalty. The developed model would be practical in helping the service providers in understanding the impacts of the identified factors in the proposed customers’ loyalty model. The outcomes of the study could also be used in the design of recommender systems and the deployed algorithm