User Navigation Research Articles

The effect of the product categories diversity recommended on cross‐buying in electronic commerce platforms: The moderating role of user navigation heterogeneity

AbstractE‐commerce platforms encourage consumers' cross‐buying behavior to boost user traffic and trading volume. However, balancing the precision and richness of recommended information with the mechanisms influencing consumer cross‐buying remains unclear, which also poses challenges to the specific operation of these platforms. Therefore, this study leverages extensive online behavioral data from an e‐commerce platform, encompassing detailed records of 26,034 consumers. By employing unsupervised machine learning algorithms to distinguish the heterogeneity of consumer browsing, we apply clickstream data to explore the mechanisms of the impact of information diversity, specifically the breadth and depth of recommended product categories, on cross‐buying. This study reveals that the effect of diverse recommendations on online decision‐making undergoes marginal variations due to alterations in both the breadth and depth of these recommendations. Specifically, the depth and breadth of recommended product categories exhibit an inverted U‐shaped relationship with cross‐buying, initially promoting, and subsequently suppressing it. Additionally, consumers with different browsing behavior characteristics respond differently to recommendation diversity. Confused visitors and hedonic visitors positively moderate the depth of information and negatively moderate the breadth of recommendations with the inverted U‐shaped effect on cross‐buying. Search‐oriented visitors positively adjust the inverted U‐shaped relationship between the depth of information and the breadth of recommendations on consumer cross‐buying. This study enriches research on cross‐buying in the domain of recommendations on e‐commerce platforms from the perspective of information diversity, providing valuable insights for optimizing recommendation strategies, promoting cross‐buying behavior, and enhancing consumer loyalty.

Impact of the mini-map on the interpretation of spatial situations in the virtual geographical space of a video game

ABSTRACT Mini-map as a world map in miniature format is often used as the most important cartographic element to facilitate the navigation and orientation of users in virtual geographical space. It provides detailed information about the position, orientation, nearby objects or terrain around the user in a compact and comprehensive way. The authors of this article wanted to find out whether the mini-map helps users with different levels of knowledge about the game to understand and interpret spatial situations that occur when playing in the virtual geographical space. The study was conducted in the form of an offline survey that required respondents to watch five short videos depicting different spatial situations during the League of Legends game. The 60 respondents were divided into 3 different groups whose responses were analyzed using statistical tests and mini-map heatmap eye-tracking analysis. The results of the study confirm that there is a correlation between the mini-map and the correctness of the interpretation of spatial situations in the game in all groups investigated. Previous gaming experiences were found to reduce the need to use a mini-map to interpret spatial situations, proving that video games provide players with comprehensive information about various events on the user interface.

Dynamic recommender system for chronic disease-focused online health community

Unequal distribution of healthcare resources poses a significant challenge in numerous regions and countries worldwide. Online Health Communities (OHCs) serve as pivotal platforms for patients to share treatment experiences and seek emotional support from peers facing similar health challenges. The facilitation of efficient user navigation to relevant sub-communities and topics within OHCs emerges as a critical need. Addressing these complexities, a recommendation system specifically designed for OHCs is proposed to tackle unique challenges such as simultaneous prediction of sub-communities and topics with varying levels of information, capturing the influence of evolving health stages on patient interactions, and ensuring coherent recommendations. To meet these challenges, OHCs are conceptualized as dynamic tripartite graphs that model three primary entities—patients, sub-communities, and topics—alongside their temporal interactions. Novel techniques including dynamic tripartite subgraph convolution and dynamic tripartite collaborative filtering are introduced to effectively capture the impact of changing health stages. Additionally, a multi-type entity prediction method is developed to forecast sub-communities and topics, incorporating robust constraints to ensure the validity of predictions. The study conducts extensive experiments using real-world datasets from two online chronic disease communities, including the Breast OHC dataset and the Bladder OHC dataset, encompassing 5,346,591 and 16,427 patient interactions, respectively. Results demonstrate significant advancements over state-of-the-art methods, achieving maximum improvements of 11% and 15% in Mean Reciprocal Rank and Recall metrics, respectively. Sensitivity analyses of online and batch-based algorithms provide insights into the trade-offs between efficiency and accuracy, supported by an illustrative application example highlighting the system’s superiority over existing approaches.

NavigateMe : An In-Building Navigation Application

For some people to reach their destination and complete their daily tasks, navigation systems serve as an aid, a tool, or an assistance. Currently, International Islamic University Malaysia (IIUM) lacks an in-building navigation system that could assist users, particularly new students, in finding their way to the location of their choice. This paper has two goals. This study first examines the current campus navigation systems and the relevant research article. Second, this work suggests a mobile navigation system that facilitates user navigation within Kuliyyah of Information and Communication Technology (KICT) facilities. Methods used to build the application were according to Software Development Life Cycle (SDLC). The suggested navigation application, NavigateMe, helps users navigate within buildings by providing sequential instructions and images of landmarks along the route. It stands out for being usable even in the event of sporadic internet connectivity. In conclusion, visitors and the IIUM community, particularly the new students of KICT, will benefit from an in-building navigation application like NavigateMe to effortlessly reach their destination.

Mobile AR Interaction Design Patterns for Storytelling in Cultural Heritage: A Systematic Review

The recent advancements in mobile technologies have enabled the widespread adoption of augmented reality (AR) to enrich cultural heritage (CH) digital experiences. Mobile AR leverages visual recognition capabilities and sensor data to superimpose digital elements into the user’s view of their surroundings. The pervasive nature of AR serves several purposes in CH: visitor guidance, 3D reconstruction, educational experiences, and mobile location-based games. While most literature reviews on AR in CH focus on technological aspects such as tracking algorithms and software frameworks, there has been little exploration of the expressive affordances of AR for the delivery of meaningful interactions. This paper (based on the PRISMA guidelines) considers 64 selected publications, published from 2016 to 2023, that present mobile AR applications in CH, with the aim of identifying and analyzing the (mobile) AR (interaction) design patterns that have so far been discussed sporadically in the literature. We identify sixteen (16) main UX design patterns, as well as eight (8) patterns with a single occurrence in the paper corpus, that have been employed—sometimes in combination—to address recurring design problems or contexts, e.g., user navigation, representing the past, uncovering hidden elements, etc. We analyze each AR design pattern by providing a title, a working definition, principal use cases, and abstract illustrations that indicate the main concept and its workings (where applicable) and explanation with respect to examples from the paper corpus. We discuss the AR design patterns in terms of a few broader design and development concerns, including the AR recognition approach, content production and development requirements, and affordances for storytelling, as well as possible contexts and experiences, including indoor/outdoor settings, location-based experiences, mobile guides, and mobile games. We envisage that this work will thoroughly inform AR designers and developers abot the current state of the art and the possibilities and affordances of mobile AR design patterns with respect to particular CH contexts.

Effects of Feedback Type on Touchscreen Navigation for Younger and Older Users

Abstract This study investigates how feedback from different modalities affect window navigation tasks performed on a touchscreen by younger and older users. Thirty-two younger and 32 older users moved a window toward one of four or eight target areas with no feedback or visual, tactile or visual–tactile feedback. Visual feedback produced significantly higher accuracy and shorter task completion times than tactile feedback. Generally, visual–tactile feedback did not produce statistically higher accuracy or shorter task completion time than visual feedback. However, visual–tactile feedback prevented both younger and older users from overshooting the windows. Additionally, when users attempted to move the window to one of the eight target areas, the visual–tactile feedback helped younger users perform better. According to theories about human processing, the reason for the subtle advantage of visual–tactile feedback is that the unintuitive tactile force induces a higher cognitive load than straightforward visual information during window navigation. Nevertheless, visual–tactile feedback elicited the best user experience, as measured by questionnaires on emotion and fatigue, gesture usability and preference. Furthermore, more older users prefer visual–tactile feedback than younger users. Therefore, visual–tactile feedback should be considered, and the tactile feedback should be modified to be more intuitive in window navigation tasks. Research Highlights Visual feedback improved task performance more than tactile feedback in window navigation task. Visual–tactile feedback did not improve task performance compared to visual feedback. Visual–tactile feedback prevented users from overshooting the window. More older users than younger users prefer visual–tactile feedback.

Detection and Determination of User Position Using Radio Tomography with Optimal Energy Consumption of Measuring Devices in Smart Buildings

The main objective of the research presented in the following work was the adaptation of reflection-radar technology in a detection and navigation system using radio-tomographic imaging techniques. As key aspects of this work, the energy optimization of high-frequency transmitters can be considered for use inside buildings while maintaining user safety. The resulting building monitoring and control system using a network of intelligent sensors supported by artificial intelligence algorithms, such as logistic regression or neural networks, should be considered an outcome. This paper discusses the methodology for extracting information from signal echoes and how they were transported and aggregated. The data extracted in this way were used to support user navigation through a building, optimize energy based on presence information, and increase the facility’s overall security level. A band from 5 GHz to 6 GHz was chosen as the carrier frequency of the signals, representing a compromise between energy expenditure, range, and the properties of wave behavior in contact with different types of matter. The system includes proprietary hardware solutions that allow parameters to be adjusted over the entire range and guarantee adaptation for RTI (radio tomography imaging) technology.

A multi-start simulated annealing strategy for Data Lake Organization Problem

The Data Lake Organization Problem consists of optimized data navigation structures generation to reduce the user’s time exploring all available data. The goal is to find a data organization that maximizes the expected probability of table discovery during user navigation. For this problem, we propose a simulated annealing metaheuristic and compare it with the Organize literature solution on benchmark instances. The instances are Socrata Open Data Lake samples with varying topics and open data from government entities worldwide. To validate our proposal, we performed a statistical analysis using a non-parametric test, which confirmed the dominance of our proposition over the state-of-the-art. Our proposal was more efficient and increased the expected probability of table discovery up to 15%. Thus, our strategy can find better solutions in the benchmarks evaluated even without exhaustively analyzing all of them and more effectively exploring the space of solutions.

BiRD: Using Bidirectional Rotation Gain Differences to Redirect Users during Back-and-forth Head Turns in Walking.

Redirected walking (RDW) facilitates user navigation within expansive virtual spaces despite the constraints of limited physical spaces. It employs discrepancies between human visual-proprioceptive sensations, known as gains, to enable the remapping of virtual and physical environments. In this paper, we explore how to apply rotation gain while the user is walking. We propose to apply a rotation gain to let the user rotate by a different angle when reciprocating from a previous head rotation, to achieve the aim of steering the user to a desired direction. To apply the gains imperceptibly based on such a Bidirectional Rotation gain Difference (BiRD), we conduct both measurement and verification experiments on the detection thresholds of the rotation gain for reciprocating head rotations during walking. Unlike previous rotation gains which are measured when users are turning around in place (standing or sitting), BiRD is measured during users' walking. Our study offers a critical assessment of the acceptable range of rotational mapping differences for different rotational orientations across the user's walking experience, contributing to an effective tool for redirecting users in virtual environments.

Trust in Anonymous News? How Users Navigate Political News Channels on Russian Telegram

The paper explores the phenomenon of anonymous news channels on Russian Telegram, which have become increasingly popular in recent years. Drawing on 25 self-confrontation interviews, we answer the following questions: Do users trust anonymous news? If not, why do they keep using this information source? How does a restrictive socio-political context influence users’ trust in alternative news sources? Our results show that, in Russia, the concept of trust is linked to the normative democratic understanding of journalistic functions. At the same time, many users believe that trust in media is not at all necessary and develop individual strategies to navigate a “chaos of narratives”. The paper discusses Telegram’s role in shaping trust or distrust in news.

Knowledge-driven profile dynamics

In the last decades, user profiles have been used in several areas of information technology. In the literature, most research works, and systems focus on the creation of profiles (using Data Mining techniques based on user's navigation or interaction history). In general, the dynamics of profiles are made by means of a systematic recreation of the profiles, without using the previous profiles. In this paper we propose to formalize the creation, representation, and dynamics of profiles from a Knowledge-Driven perspective. We introduce and axiomatically characterize four operators for changing profiles using a belief change inspired approach.

Do popular apps have issues regarding energy efficiency?

Mobile apps have become essential components of our daily lives, seamlessly integrating into routines to fulfill communication, productivity, entertainment, and commerce needs, with their diverse range categorized within app stores for easy user navigation and selection. User reviews and ratings play a crucial role in app selection, significantly influencing user decisions through the interplay between feedback and quantified satisfaction. The emphasis on energy efficiency in apps, driven by the limited battery lifespan of mobile devices, impacts app ratings by potentially prompting users to assign low scores, thereby influencing the choices of others. In this study, the presence of energy consumption issues within widely-used popular apps that have high app ratings and user interaction has been investigated through the analysis of user reviews. It is anticipated that popular apps, with high ratings, are less problematic than other apps. User reviews were collected from 32 apps across 16 diverse categories and subsequently filtered based on specific keywords. From the resulting pool of 14,064 user reviews, 8,007 reviews were manually identified as specifically addressing the app's energy consumption. The results of the study demonstrate that all 32 popular apps under consideration exhibit issues related to energy consumption. While the frequency of energy-related issues may vary, it is evident that users are concerned about app energy consumption, as evidenced by the reception of complaint reviews regarding their energy usage. App energy efficiency is important to users, including popular apps with diverse features, necessitating developers to address expectations and optimize for energy efficiency. Improving the energy efficiency of apps has the potential to enhance user satisfaction and, consequently, contribute to the overall success of the app.

A User-Centered Privacy Policy Management System for Automatic Consent on Cookie Banners

Despite growing concerns about privacy and an evolution in laws protecting users’ rights, there remains a gap between how industries manage data and how users can express their preferences. This imbalance often favors industries, forcing users to repeatedly define their privacy preferences each time they access a new website. This process contributes to the privacy paradox. We propose a user support tool named the User Privacy Preference Management System (UPPMS) that eliminates the need for users to handle intricate banners or deceptive patterns. We have set up a process to guide even a non-expert user in creating a standardized personal privacy policy, which is automatically applied to every visited website by interacting with cookie banners. The process of generating actions to apply the user’s policy leverages customized Large Language Models. Experiments demonstrate the feasibility of analyzing HTML code to understand and automatically interact with cookie banners, even implementing complex policies. Our proposal aims to address the privacy paradox related to cookie banners by reducing information overload and decision fatigue for users. It also simplifies user navigation by eliminating the need to repeatedly declare preferences in intricate cookie banners on every visited website, while protecting users from deceptive patterns.

Beyond the Hype-The Actual Role and Risks of AI in Today's Medical Practice: Comparative-Approach Study.

The evolution of artificial intelligence (AI) has significantly impacted various sectors, with health care witnessing some of its most groundbreaking contributions. Contemporary models, such as ChatGPT-4 and Microsoft Bing, have showcased capabilities beyond just generating text, aiding in complex tasks like literature searches and refining web-based queries. This study explores a compelling query: can AI author an academic paper independently? Our assessment focuses on four core dimensions: relevance (to ensure that AI's response directly addresses the prompt), accuracy (to ascertain that AI's information is both factually correct and current), clarity (to examine AI's ability to present coherent and logical ideas), and tone and style (to evaluate whether AI can align with the formality expected in academic writings). Additionally, we will consider the ethical implications and practicality of integrating AI into academic writing. To assess the capabilities of ChatGPT-4 and Microsoft Bing in the context of academic paper assistance in general practice, we used a systematic approach. ChatGPT-4, an advanced AI language model by Open AI, excels in generating human-like text and adapting responses based on user interactions, though it has a knowledge cut-off in September 2021. Microsoft Bing's AI chatbot facilitates user navigation on the Bing search engine, offering tailored search. In terms of relevance, ChatGPT-4 delved deeply into AI's health care role, citing academic sources and discussing diverse applications and concerns, while Microsoft Bing provided a concise, less detailed overview. In terms of accuracy, ChatGPT-4 correctly cited 72% (23/32) of its peer-reviewed articles but included some nonexistent references. Microsoft Bing's accuracy stood at 46% (6/13), supplemented by relevant non-peer-reviewed articles. In terms of clarity, both models conveyed clear, coherent text. ChatGPT-4 was particularly adept at detailing technical concepts, while Microsoft Bing was more general. In terms of tone, both models maintained an academic tone, but ChatGPT-4 exhibited superior depth and breadth in content delivery. Comparing ChatGPT-4 and Microsoft Bing for academic assistance revealed strengths and limitations. ChatGPT-4 excels in depth and relevance but falters in citation accuracy. Microsoft Bing is concise but lacks robust detail. Though both models have potential, neither can independently handle comprehensive academic tasks. As AI evolves, combining ChatGPT-4's depth with Microsoft Bing's up-to-date referencing could optimize academic support. Researchers should critically assess AI outputs to maintain academic credibility.

Frequency Bias Mitigation for Surface User Navigation Using Double Differencing Joint Doppler and Ranging

Frequency Bias Mitigation for Surface User Navigation Using Double Differencing Joint Doppler and Ranging

Advancing E-Commerce Analytics The Development of Intelligent Analytics Software for Enhanced Customer Experience

In the current era of rapid growth in electronic commerce, it is crucial for organizations to possess an in-depth understanding of client habits in order to effectively optimize their plans. This position paper presents a proposal that centers on the creation of Intelligent Analytics Software designed specifically for Casper, a well-known e-commerce platform that specializes in the selling of electronic devices. The main purpose of the software is to facilitate the real-time monitoring and analysis of users' browsing patterns, utilizing historical navigation data as a basis. The effort arises from the growing necessity to utilize data-driven insights in order to improve client experiences and achieve commercial advantages. The statement above highlights the need for secure and accurate information collecting, rapidly adapting to user behaviors in real-time, and delivering personalized benefits, as well as effectively addressing platform-related challenges. Our research and development efforts involve conducting comprehensive analyses of user navigation, ensuring secure processing of data, and effectively managing behavioral data. The project is notable for its implementation of innovative analytical software that enables real-time tracking of consumer activity, generation of analytical reports, identification of consecutive actions using Sankey diagrams, segmentation analysis, and production of heatmaps. Moreover, the program enhances cross-selling capabilities by monitoring often co-purchased items, so providing a useful asset to the e-commerce platform's repertoire. In addition to expanding the company's understanding of client habits and broadening its range of products, the project also serves to advance national expertise and pursue international academic contributions via research papers. The success criteria include strong benchmarks for clustering and association analysis techniques, emphasizing the dedication to providing fast analytical solutions. The paper offers a comprehensive overview of the project's significance, objectives, methodology, and the urgent need it answers within the field of e-commerce analytics.

DESIGN AND DEVELOPMENT OF AN INTEGRATED MOBILE APPLICATION COMBINING THE FEATURES OF 2GIS, ZENLY, AND GOOGLE MAPS

This article focuses on creating a universal mobile application that combines the power of 2GIS, Zenly and Google Maps to optimize user navigation and improve the location experience. This innovative application integrates the strengths of these platforms, simplifying navigation, route planning, real-time tracking of public transport and promoting social connectivity. The study covers the design, implementation and evaluation of this innovative solution, highlighting its potential to revolutionize modern location services. The main goal of the project is to create and evaluate a comprehensive mobile application that provides users with a single tool for location services, integrating the core functions of 2GIS, Zenly and Google Maps. The app's design pays attention to usability, using detailed Google Maps for accurate and up-todate location information. In summary, this study represents a significant contribution to the field of mobile navigation applications, demonstrating the potential to transform modern location services.

DESIGNING INVENTORY INFORMATION SYSTEM AT PT HAADI WAHANA JAYA DESKTOP BASED

This research documents the implementation process of a desktop-based inventory information system at PT Haadi Wahana Jaya with the aim of increasing the efficiency and accuracy of inventory management. Through a research and development approach, this system is designed to meet the company's specific needs. With an intuitive user interface design, the system provides easy access and navigation for users. The login form allows users to access inventory information securely, with the implementation of additional security features such as CAPTCHA. In addition, integration with financial and production systems ensures interdepartmental connectivity and better coordination. Implementation results show increased operational efficiency, optimization of inventory levels, and accurate real-time reporting. Although some challenges in terms of culture change were encountered, user feedback indicated a higher level of satisfaction. The conclusion of this research underlines the positive contribution of desktop-based inventory information systems to PT Haadi Wahana Jaya's inventory management.

Trends in usage of navigation and robotic assistance in elective spine surgeries: a study of 105,212 cases from 2007 to 2015.

Identify trends of navigation and robotic-assisted elective spine surgeries. Elective spine surgery patients between 2007 and 2015 in the Nationwide Inpatient Sample (NIS) were isolated by ICD-9 codes for Navigation [Nav] or Robotic [Rob]-Assisted surgery. Basic demographics and surgical variables were identified via chi-squared and t tests. Each system was analyzed from 2007 to 2015 for trends in usage. Included 3,759,751 patients: 100,488 Nav; 4724 Rob. Nav were younger (56.7 vs 62.7years), had lower comorbidity index (1.8 vs 6.2, all p < 0.05), more decompressions (79.5 vs 42.6%) and more fusions (60.3 vs 52.6%) than Rob. From 2007 to 2015, incidence of complication increased for Nav (from 5.8 to 21.7%) and Rob (from 3.3 to 18.4%) as well as 2-3 level fusions (from 50.4 to 52.5%) and (from 1.3 to 3.2%); respectively. Invasiveness increased for both (Rob: from 1.7 to 2.2; Nav: from 3.7 to 4.6). Posterior approaches (from 27.4 to 41.3%), osteotomies (from 4 to 7%), and fusions (from 40.9 to 54.2%) increased in Rob. Anterior approach for Rob decreased from 14.9 to 14.4%. Nav increased posterior (from 51.5% to 63.9%) and anterior approaches (from 16.4 to 19.2%) with an increase in osteotomies (from 2.1 to 2.7%) and decreased decompressions (from 73.6 to 63.2%). From 2007 to 2015, robotic and navigation systems have been performed on increasingly invasive spine procedures. Robotic systems have shifted from anterior to posterior approaches, whereas navigation computer-assisted procedures have decreased in rates of usage for decompression procedures.

Performance Analysis of NOMA-RIS Aided Integrated Navigation and Communication (INAC) Networks

Satellite communication constitutes a promising solution for the sixth generation (6 G) wireless networks in terms of providing global communication services. In order to provide a cost-effective satellite network, we propose a novel medium-earth-orbit (MEO) satellite aided integrated-navigation-and-communication (INAC) network. To overcome the severe path loss of MEO satellites, we conceive a network for simultaneous serving navigation and communication for ground users by adopting the non-orthogonal multiple access (NOMA) technique and the reconfigurable intelligent surface technique. Based on the power allocation strategies, communication-oriented (CO-) and navigation-oriented (NO-) INAC scenarios are proposed. We first derive the closed-form expressions for the new channel statistics, outage probability and channel capacity of the INAC-user. For gleaning further insights, the diversity orders and navigation accuracy are evaluated for illustrating the performance of the INAC networks. According to our analysis, when RIS elements are sufficient, the proposed INAC network can perform better than conventional terrestrial communication networks in terms of channel capacity. Numerical results are provided for confirming that the NO-INAC and CO-INAC scenarios have superior performance for communication in the low signal-to-noise-ratio (SNR) regimes and high SNR regimes, respectively, which indicates a hybrid CO/NO-INAC network is preferable.