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Communication within online platforms supported by chatbots requires algorithms, language processing methods, and an effective visual representation. These are crucial elements for increasing user engagement and making communication more akin to natural conversation. Chatbots compete with other graphic elements within websites or applications, and thus attracting a user's attention is a challenge even before the actual conversation begins. A chatbot may remain unnoticed even with sophisticated techniques at play. Drawing attention to the chatbot area localized within the periphery area can be carried out with the use of various visual characteristics. The presented study analyzed the impact of changes in a chatbot's emotional expressions on user reaction. The aim of this study was to observe, based on user reaction times, whether changes in a chatbot's emotional expressions make it more noticeable. The results showed that users are more sensitive to positive emotions within chatbots, as positive facial expressions were noticed more quickly than negative ones. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed e-commerce customer preference monitoring (ECPM) tool can be utilized for any website, but especially e-commerce websites, for collecting a stream of user-activity data, which could be used to adapt an interface to fit user needs in real time. Besides personalizing a website interface, observing user activity can provide information about their preferences, which can be used to dynamically individualize different type of content presented to user, e.g., recommendations of products. Observing user interactions with computer interfaces can lead to better understanding of user needs and, thus, build an intelligent recommender system which will understand and guide each user through the cognitive process of discovering products that would meet individual preferences. Recommender systems play a vital role in e-commerce by increasing the likelihood of transactions and improving sales thanks to presenting personal recommendations. Due to the marketing habituation effect, users are less and less responsive to this type of content. Visual recommendation presentation, in particular the recommendation zone layout can influence the effectiveness of a recommendation. This study examines human–computer interactions for vertical, horizonal, and mixed layouts of recommending interfaces of four major e-commerce stores, and is based on our document object model events-based behavior analysis tool. Results from this implicit feedback study are presented and analyzed, showing that vertical recommendation zones attracted more attention than horizontal ones. [ABSTRACT FROM AUTHOR]

Copyright of Colloquia Germanica Stetinensia is the property of Wydawnictwo Naukowe Uniwersytetu Szczecinskiego / University of Szczecin Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Colloquia Germanica Stetinensia is the property of Wydawnictwo Naukowe Uniwersytetu Szczecinskiego / University of Szczecin Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, the global e-commerce landscape has witnessed rapid growth, with sales reaching a new peak in the past year and expected to rise further in the coming years. Amid this e-commerce boom, accurately predicting user purchase behavior has become crucial for commercial success. We introduce a novel framework integrating three innovative approaches to enhance the prediction model's effectiveness. First, we integrate an event-based timestamp encoding within a time-series attention model, effectively capturing the dynamic and temporal aspects of user behavior. This aspect is often neglected in traditional user purchase prediction methods, leading to suboptimal accuracy. Second, we incorporate Graph Neural Networks (GNNs) to analyze user behavior. By modeling users and their actions as nodes and edges within a graph structure, we capture complex relationships and patterns in user behavior more effectively than current models, offering a nuanced and comprehensive analysis. Lastly, our framework transcends traditional learning strategies by implementing advanced meta-learning techniques. This enables the model to autonomously adjust learning parameters, including the learning rate, in response to new and evolving data environments, thereby significantly enhancing its adaptability and learning efficiency. Through extensive experiments on diverse real-world e-commerce datasets, our model demonstrates superior performance, particularly in accuracy and adaptability in large-scale data scenarios. This study not only overcomes the existing challenges in analyzing e-commerce user behavior but also sets a foundation for future exploration in this dynamic field. We believe our contributions provide significant insights and tools for e-commerce platforms to better understand and cater to their users, ultimately driving sales and improving user experiences. [ABSTRACT FROM AUTHOR]

Copyright of Miedzy Oryginalem a Przekladem is the property of Ksiegarnia Akademicka Sp. zo.o and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

High-quality academic publishing is built on rigorous peer review. I Big Data and Cognitive Computing (BDCC) i was able to uphold its high standards for published papers due to the outstanding efforts of our reviewers. MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. [Extracted from the article]

Thanks to the efforts of our reviewers in 2022, the median time to first decision was 17 days and the median time to publication was 37 days. Regardless of whether the articles they examined were ultimately published, the editors would like to express their appreciation and thank the following reviewers for the time and dedication that they have shown I Mathematics i : those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). I Mathematics i was able to uphold its high standards for published papers due to the outstanding efforts of our reviewers. [Extracted from the article]

The editors would like to express their sincere gratitude to the following reviewers for their precious time and dedication, regardless of whether the papers were finally published: Peer review is the driving force of journal development, and reviewers are gatekeepers who ensure that I Applied Sciences i maintains its standards for the high quality of its published papers. [Extracted from the article]

Smart manufacturing system must be designed based on a set of strategic objectives and a variety of surrounding environments; therefore, it includes a series of complex decisions and trade-offs between investment and profit. The MSDD based on the axiomatic design principle has an advantage on breaking down requirements and parameters and considering the relationships of them, but there is a limit to quantify relative weights of each requirement and parameter. To complement the limitation, a quantification method applying the analytic network process is proposed. The proposed method can calculate the relative importance degrees of the requirement and parameter objectively and help us understand and focus on more important factors of the smart manufacturing system design. [ABSTRACT FROM AUTHOR]

Thanks to the great efforts of our reviewers, I Information i was able to maintain its standards for the high quality of its published papers. Thanks to the contribution of our reviewers, in 2021, the median time to first decision was 17 days and the median time to publication was 38 days. [Extracted from the article]

Symmetry 2021, 13, 181. https://doi.org/10.3390/sym13020181 www.mdpi.com/journal/symmetry Editorial Acknowledgment to Reviewers of Symmetry in 2020 Symmetry Editorial Office MDPI AG, St. Alban-Anlage 66, 4052 Basel, Switzerland Peer review is the driving force of journal development, and reviewers are gatekeepers who ensure that Symmetry maintains its standards for the high quality of its published papers. [Extracted from the article]