Understanding Preferences in Tourism Email Marketing

Nowadays, advanced tools allow the personalization of email communication with tourism clients or prospects based on explicit (e.g. name, age, language, country) and implicit indicators (e.g. ranking of activity in the opening rate of the newsletter, browsing preferences, online preferences provided by cookies, etc.). However, knowing how audiences react to emails allows Destination Marketing Organizations (DMOs) to create content clusters for personalized communication. The purpose of this study is to empirically investigate the preferences on tourism email marketing of different audiences based on a specific explicit indicator, namely the language chosen by users to receive communications by a DMO. A content analysis on a longitudinal dataset based on 131 newsletter messages sent between 2018 and 2021 to more than 50′000 contacts by a DMO in Switzerland was performed. Results show that content should be adapted to different audiences speaking different languages instead of providing just a translation. Specifically, the German-speaking audience seems to be more inclined to messages that focus on winter sports and hiking, the Italian-speaking audience to news about hiking and information on COVID-19, the French-speaking audience to news about promotions, while the English-speaking audience to contents on discounts and COVID-19-related. These results provide an important contribution to studies on tourism personalization of communication in the context of email marketing, suggesting the role of content adaptation according to the language and cultural background of the audience. DMO managers can also benefit from this research in understanding how to address a similar study on their datasets and compare the emerged content clusters.

Page-Level Main Content Extraction From Heterogeneous Webpages

The main content of a webpage is often surrounded by other boilerplate elements related to the template, such as menus, advertisements, copyright notices, and comments. For crawlers and indexers, isolating the main content from the template and other noisy information is an essential task, because processing and storing noisy information produce a waste of resources such as bandwidth, storage space, and computing time. Besides, the detection and extraction of the main content is useful in different areas, such as data mining, web summarization, and content adaptation to low resolutions. This work introduces a new technique for main content extraction. In contrast to most techniques, this technique not only extracts text, but also other types of content, such as images, and animations. It is a Document Object Model-based page-level technique, thus it only needs to load one single webpage to extract the main content. As a consequence, it is efficient enough as to be used online (in real-time). We have empirically evaluated the technique using a suite of real heterogeneous benchmarks producing very good results compared with other well-known content extraction techniques.

A Computationally Efficient User Model for Effective Content Adaptation Based on Domain-Wise Learning Style Preferences: A Web-Based Approach

In the educational hypermedia domain, adaptive systems try to adapt educational materials according to the required properties of a user. The adaptability of these systems becomes more effective once the system has the knowledge about how a student can learn better. Studies suggest that, for effective personalization, one of the important features is to know precisely the learning style of a student. However, learning styles are dynamic and may vary domain-wise. To address such aspects of learning styles, we have proposed a computationally efficient solution that considers the dynamic and nondeterministic nature of learning styles, effect of the subject domain, and nonstationary aspect during the learning process. The proposed model is novel, robust, and flexible to optimize students’ domain-wise learning style preferences for better content adaptation. We have developed a web-based experimental prototype for assessment and validation. The proposed model is compared with the existing available learning style-based model, and the experimental results show that personalization based on incorporating discipline-wise learning style variations becomes more effective.

The impacts of architecture and decorative arts on health based on medical sources in the Muslim societies during the medieval era

This article aims to provide a historical overview of the impact of architecture and decorative arts on health and health preservation in Muslim societies during the medieval era. Based on primary medical sources, this article provides a historical interpretation of the theoretical origin of the ignored link between medicine and architecture (and decorative arts). Our findings indicate that some empirical results concerning the effects and aspects of built environments (architectural spaces) on health and treatment–both physical and mental– have been considered in the medical sources. Practical instructions of these sources introduced two theoretical achievements: 1) an introduction to the historical knowledge of environmental health and design of healthy places, and 2) a comparative analogy of the built environment and human nature (organism), which became a theoretical basis for the relationship between natural sciences, architecture, and the decorative arts in the middle ages. Considerations of the study show the extent to which architects and artisans, based on the teachings and instructions of physicians, dealt with the structural and content adaptation models of architecture and decorative arts to human organism and nature.

Content Adaptation and Depth Perception in an Affordable Multi-View Display

We present SliceView, a simple and inexpensive multi-view display made with multiple parallel translucent sheets that sit on top of a regular monitor; each sheet reflects different 2D images that are perceived cumulatively. A technical study is performed on the reflected and transmitted light for sheets of different thicknesses. A user study compares SliceView with a commercial light-field display (LookingGlass) regarding the perception of information at multiple depths. More importantly, we present automatic adaptations of existing content to SliceView: 2D layered graphics such as retro-games or painting tools, movies and subtitles, and regular 3D scenes with multiple clipping z-planes. We show that it is possible to create an inexpensive multi-view display and automatically adapt content for it; moreover, the depth perception on some tasks is superior to the one obtained in a commercial light-field display. We hope that this work stimulates more research and applications with multi-view displays.

Low-delay single holoscopic 3D computer-generated image to multiview images

Due to the nature of holoscopic 3D (H3D) imaging technology, H3D cameras can capture more angular information than their conventional 2D counterparts. This is mainly attributed to the macrolens array which captures the 3D scene with slightly different viewing angles and generates holoscopic elemental images based on fly’s eyes imaging concept. However, this advantage comes at the cost of decreasing the spatial resolution in the reconstructed images. On the other hand, the consumer market is looking to find an efficient multiview capturing solution for the commercially available autostereoscopic displays. The autostereoscopic display provides multiple viewers with the ability to simultaneously enjoy a 3D viewing experience without the need for wearing 3D display glasses. This paper proposes a low-delay content adaptation framework for converting a single holoscopic 3D computer-generated image into multiple viewpoint images. Furthermore, it investigates the effects of varying interpolation step sizes on the converted multiview images using the nearest neighbour and bicubic sampling interpolation techniques. In addition, it evaluates the effects of changing the macrolens array size, using the proposed framework, on the perceived visual quality both objectively and subjectively. The experimental work is conducted on computer-generated H3D images with different macrolens sizes. The experimental results show that the proposed content adaptation framework can be used to capture multiple viewpoint images to be visualised on autostereoscopic displays.