Individual User Preferences Research Articles

Enhancing User Experience through AI-Driven Personalization in User Interfaces

Artificial intelligence (AI) has revolutionized user interface (UI) design by introducing personalization techniques that cater to individual user preferences, behaviors, and contexts. This research explores the integration of AI-driven personalization in user interfaces to enhance user experience (UX), focusing on adaptive design, predictive analytics, and real-time customization. By leveraging machine learning algorithms and behavioral data, AI enables interfaces to evolve dynamically, aligning with the unique needs of each user. This study investigates the role of personalization in improving engagement, satisfaction, and efficiency across various applications, such as e-commerce platforms, healthcare systems, and educational tools. Additionally, it examines the challenges of implementing personalized interfaces, including privacy concerns, data ethics, and algorithmic bias. By addressing these challenges, the research aims to develop best practices for ethical AI integration in user-centered design. The findings contribute to the growing body of knowledge on AI’s transformative potential in creating intuitive, efficient, and user-friendly interfaces, ultimately redefining the standards for digital interaction.

Centralized Platform for Streamlining Maharashtra Tourism

This paper introduces a centralized travel planner platform specifically designed to cater to tourists exploring Maharashtra's forts, temples, and beaches. The platform offers personalized travel packages tailored to individual user preferences, featuring options such as guide assignments, secure travel routes, recommendations for nearby restaurants and accommodations, and notable attractions in the vicinity. It addresses the challenges posed by the fragmented nature of current travel planning resources, particularly for those interested in Maharashtra’s rich cultural and natural landmarks. By incorporating a recommendation system, customizable packages, and real-time data integration, the platform provides a seamless and comprehensive travel planning experience. Preliminary tests suggest that the platform significantly improves the user experience by simplifying the process of organizing trips. Key Words: Maharashtra tourism, local travel planning, forts, temples, beaches, personalized recommendations.

Trade-off between herbal and allopathic treatments: An ethnopharmacological case study in Rema-kalenga wildlife sanctuary, Bangladesh

The Rema-Kalenga Wildlife Sanctuary (RKWS) is a protected forest in Bangladesh that houses a variety of rare flora and fauna and supports the sustenance of 13 ethnic communities. This forest's indigenous and other inhabitants traditionally have a strong cultural connection to the plants, particularly medicinal plants. Due to modern allopathic medicine's rapid growth and commercial tree species prioritization, many medicinal plants are now on the verge of endangerment. Under such circumstances, it is crucial to explore how the local community perceives the importance of herbal treatments in contrast to allopathy, the underlying reasons for their perceptions, and the specific ailments for which they use the plants. The main objectives are: 1) to evaluate the perceptions of the local community towards allopathy and herbal medicine using Nussbaum's central capabilities approach, 2) to identify medicinal plant diversity, therapeutic usages, and quantitative indices, 3) to determine the factors that influence the use of medicinal plants. Repeated interviews and field surveys were conducted at the RKWS, interviewing 145 people, including the indigenous community (72.42 %) and traditional healers (8.27 %) from the surrounding seven villages. The study identified 51 medicinal plant species belonging to 39 families for their potent medicinal properties in treating various ailments. The predominant parts of the plants used in the treatments were leaves and roots. The uses were classified into 12 categories according to Nussbaum's central capabilities. The findings identified Aloe vera, Phyllanthus emblica, and Azadirachta indica as highly culturally important species. In contrast, Terminalia arjuna, Swertia chirata, and Azadirachta indica were found to have the highest relative importance. The underlying determinants influencing the preferences of individual users towards herbal medicine were income from agroforestry, beliefs, knowledge, and ethnicity, as revealed by the analysis of the ordinal logit model. The respondents viewed their strong inclination toward herbal medicine with many positive attitudes. Herbal medicine users held a negative perception of allopathy except for affiliation and practical reasons being viewed as the positive outcomes. Conversely, allopathic medicine users expressed mixed perceptions towards the treatment, with both positive and negative aspects being identified. Promoting the sustainable use of medicinal plants and their conservation efforts is imperative for the benefit of present and future generations in this region.

MRAM: Multi-scale Regional Attribute-weighting via Meta-learning for Personalized Image Aesthetics Assessment

Image aesthetics assessment (IAA) is inherently subjective, with generic image aesthetics assessment (GIAA) often overlooking individual user preferences. Personalized aesthetic assessment (PIAA) addresses this by accounting for user-specific inconsistencies. However, the limited annotated data from individual users and the scarcity of user-jointly annotated image data pose challenges. To cope with these problems, we propose a multi-scale regional attribute-weighting via meta-learning for PIAA, namely MRAM, which aims to simulate how the brain computes aesthetic values from visual stimuli. The human brain is to predict the subjective aesthetic value of the individual to the image by combining the features of the visual scene segmentation through the weighting method. MRAM introduces a multi-scale attention module to capture visual scene regions segmented at different scales, adjusting feature selection based on user attention. Additionally, a regional attribute-weighting module (RAM) is proposed to weigh these features using aesthetic attribute representations. In the meta-training phase, MRAM learns aesthetic prior knowledge from a diverse set of PIAA tasks. Transitioning to the personalization phase, MRAM is fine-tuned with only a small number of specific user samples, enabling it to rapidly adapt to the unique aesthetic preferences of individuals. Experimental results demonstrate the superior performance of the proposed MRAM compared to state-of-the-art PIAA methods.

HyperCLR: A Personalized Sequential Recommendation Algorithm Based on Hypergraph and Contrastive Learning

Sequential recommendations aim to predict users’ next interactions by modeling their interaction sequences. Most existing work concentrates on user preferences within these sequences, overlooking the complex item relationships across sequences. Additionally, these studies often fail to address the diversity of user interests, thus not capturing their varied latent preferences effectively. To tackle these problems, this paper develops a novel recommendation algorithm based on hypergraphs and contrastive learning named HyperCLR. It dynamically incorporates the time and location embeddings of items to model high-order relationships in user preferences. Moreover, we developed a graph construction approach named IFDG, which utilizes global item visit frequencies and spatial distances to discern item relevancy. By sampling subgraphs from IFDG, HyperCLR can align the representations of identical interaction sequences closely while distinguishing them from the broader global context on IFDG. This approach enhances the accuracy of sequential recommendations. Furthermore, a gating mechanism is designed to tailor the global context information to individual user preferences. Extensive experiments on Taobao, Books and Games datasets have shown that HyperCLR consistently surpasses baselines, demonstrating the effectiveness of the method. In particular, in comparison to the best baseline methods, HyperCLR demonstrated a 29.1% improvement in performance on the Taobao dataset.

User-centered Social Interaction Design in Intelligent Personal Assistants and Social Robots

User-centered design practices are used to develop the social interaction characteristics of intelligent personal assistants and social robots. In this approach, developers establish characteristics favored by the majority of users. However, catering to target users may negate the benefits in user experience and use intention achieved from matching a product’s characteristics to the preferences of individual users. The present study examines differences between target versus individual user based approaches to user-centered design. Specifically, a framework is presented on how to develop a product to meet individual user preferences. Results show that individual user preferences, while similar to the target user preferences, can vary significantly from the baseline. Machine learning and artificial intelligence approaches should be employed to help adjust social interaction approaches from a baseline to individual user preferences.

Challenges Experienced by Saudi Patients With Cancer and Their Family Caregivers in Using Digital Healthcare Technology Platforms in the COVID-19 Pandemic.

COVID-19 has provided a unique boost to the use of digital healthcare technology, putting many vulnerable people at risk of digital exclusion. To promote digital healthcare equity, it is important to identify the challenges that may inhibit cancer patients and family caregivers from benefiting from such technology. This study explored the challenges that cancer patients and family caregivers experience in using digital healthcare technology platforms during the COVID-19 pandemic. A qualitative descriptive study using face-to-face semistructured individual interviews was carried out. A purposive sample of 21 participants was recruited from a public cancer hospital in Saudi Arabia. Qualitative content analysis with an inductive approach was utilized. The factors that challenged the ability of participants to benefit from digital healthcare technology were similar. Four themes related to the challenges the two groups experienced emerged: access to platforms, use of platforms for cancer health-related purposes, attitudes toward these platforms, and individual user preferences. This study identified numerous areas for improvement regarding digital healthcare technology platform implementation, which could increase future benefits and equal use. This study's findings also provide useful information to investigators who intend to create digital nursing interventions for both groups amid COVID-19 and other worldwide health crises.

Parametric Design of an Advanced Multi-Axial Energy-Storing-and-Releasing Ankle–Foot Prosthesis

The ankle joint is pivotal in prosthetic feet, especially in Energy-Storing-and-Releasing feet, favoured by individuals with moderate to high mobility (K3/K4) due to their energy efficiency and simple construction. ESR feet, mainly designed for sagittal-plane motion, often exhibit high stiffness in other planes, leading to difficulties in adapting to varied ground conditions, potentially causing discomfort or pain. This study aims to present a systematic methodology for modifying the ankle joint’s stiffness properties across its three motion planes, tailored to individual user preferences, and to decouple the sagittal-plane behaviour from the frontal and transverse ones. To integrate the multi-axial ankle inside the MyFlex-η, the designing of experiments using finite element analysis was conducted to explore the impact of geometric parameters on the joint’s properties with respect to design constraints and to reach the defined stiffness targets on the three ankle’s motion planes. A prototype of the multi-axial ankle joint was then manufactured and tested under FEA-derived load conditions to validate the final configuration chosen. Composite elastic elements and complementary parts of the MyFlex-η, incorporating the multi-axial ankle joint, were developed, and the prosthesis was biomechanically tested according to lower limb prosthesis ISO standards and guidelines from literature and the American Orthotic and Prosthetic Association (AOPA). Experimental tests showed strong alignment with numerical predictions. Moreover, implementing the multi-axial ankle significantly increased frontal-plane compliance by 414% with respect to the same prosthesis with only one degree of freedom on the sagittal plane without affecting the main plane of locomotion performance.

Enhancing user engagement and satisfaction through personalized news recommendation systems

Personalized news recommendation systems have emerged as essential tools in addressing information overload by tailoring news content to individual user preferences. This paper provides a comprehensive overview of the advanced techniques employed in these systems, their impacts on user engagement, and the ethical considerations surrounding their development and implementation. We delve into the intricacies of data collection, processing, and user profiling, highlighting the methodologies and challenges inherent in each stage. Additionally, we explore advanced algorithmic foundations, including collaborative filtering, content-based filtering, and hybrid methods, elucidating their strengths and limitations. Furthermore, we examine the dynamics of user engagement within personalized news recommendation systems, analyzing key metrics and the role of user feedback in refining recommendation algorithms. Finally, we address privacy concerns, data sparsity issues, and biases, proposing solutions to mitigate ethical challenges and uphold user trust and fairness. This paper serves as a comprehensive guide for researchers, practitioners, and policymakers navigating the complexities of personalized news recommendation systems.

KGR: A Kernel-Mapping Based Group Recommender System Using Trust Relations

A massive amount of information explosion over the internet has caused a possible difficulty of information overload. To overcome this, Recommender systems are systematic tools that are rapidly being employed in several domains such as movies, travel, E-commerce, and music. In the existing research, several methods have been proposed for single-user modeling, however, the massive rise of social connections potentially increases the significance of group recommender systems (GRS). A GRS is one that jointly recommends a list of items to a collection of individuals based on their interests. Moreover, the single-user model poses several challenges to recommender systems such as data sparsity, cold start, and long tail problems. On the contrary hand, another hotspot for group-based recommendation is the modeling of user preferences and interests based on the groups to which they belong using effective aggregation strategies. To address such issues, a novel “KGR” group recommender system based on user-trust relations is proposed in this study using kernel mapping techniques. In the proposed model, user-trust networks or relations are exploited to generate trust-based groups of users which is one of the important behavioral and social aspects. More precisely, in KGR the group kernels and group residual matrices are exploited as well as seeking a multi-linear mapping between encoded vectors of group-item interactions and probability density function indicating how groups will rate the items. Moreover, to emphasize the relevance of individual preferences of users in a group to which they belong, a hybrid approach is also suggested in which group kernels and individual user kernels are merged as additive and multiplicative models. Furthermore, the proposed KGR is validated on two different trust-based datasets including Film Trust and CiaoDVD. In addition, KGR outperforms with an RMSE value of 0.3306 and 0.3013 on FilmTrust and CiaoDVD datasets which are lower than the 1.8176 and 1.1092 observed with the original KMR.

Shrinking VOD Traffic via Rényi-Entropic Optimal Transport

In response to the exponential surge in Video on Demand (VOD) traffic, numerous research endeavors have concentrated on optimizing and enhancing infrastructure efficiency. In contrast, this paper explores whether users' demand patterns can be shaped to reduce the pressure on infrastructure. Our main idea is to design a mechanism that alters the distribution of user requests to another distribution which is much more cache-efficient, but still remains 'close enough' (in terms of cost) to fulfil individual user's preference.

A Novel Constraint-Aware Flexible Model with Ant Colony Optimization for Symmetrical Travel Recommendation

This paper proposes a flexible travel recommender model (FTRM) that emphasizes the symmetry between user preferences and travel constraints, addressing key challenges in the field such as the integration of diverse constraint types and the customization of travel itineraries. The key contribution of the proposed model lies in its integration with the item constraints data model (ICDM), which effectively manages a plethora of constraint types. Additionally, this study develops a novel algorithm inspired by ant colony optimization (ACO) principles, demonstrating performance metrics that are comparable to state-of-the-art algorithms in this field. A comprehensive set of systematic experimental analyses is conducted, employing various models across diverse situational contexts, with the primary goal of illustrating the capabilities of the proposed symmetrical FTRM using real-world data from the Durham dataset. The obtained results highlight the model’s ability to accommodate diverse constraint types, facilitating the customization of travel itineraries to suit individual user preferences and achieve a balanced and symmetrical travel experience. Specifically, our model outperforms existing models in terms of flexibility and customization, showing significant improvements in user satisfaction and itinerary efficiency.

Personalized Recommendation of English Online Teaching Content Based on Logistic Regression Algorithm

English online teaching content encompasses a wide range of materials designed to facilitate language learning in digital environments. These resources include interactive lessons, video tutorials, audio recordings, quizzes, vocabulary exercises, grammar explanations, and authentic texts such as articles, short stories, and dialogues. The content aims to cover various language skills, including listening, speaking, reading, and writing, catering to learners of different proficiency levels and learning styles. This paper introduces a Cross-Domain Personalized Recommendation System (CDPRS) designed to enhance the delivery of English language teaching content. Leveraging machine learning algorithms and data analytics techniques, the CDPRS tailors recommendations to individual user preferences, learning objectives, and proficiency levels across diverse content domains. Through a series of analyses and evaluations, including regression analysis and simulation studies, the effectiveness and utility of the CDPRS are demonstrated. Through a series of analyses and evaluations, including regression analysis and simulation studies, the effectiveness and utility of the CDPRS are demonstrated. Results indicate that the system achieves high levels of accuracy (mean accuracy = 0.85), diversity (mean diversity = 0.72), novelty (mean novelty = 0.78), and user satisfaction (mean satisfaction = 0.82) in providing personalized recommendations.

A Review on Voice Navigation System for the Visually Impaired

A ground-breaking voice navigation system seems as a ray of light in a world where blind people must overcome enormous obstacles to navigate strange places. For those with vision impairments, safely and independently navigating in unfamiliar situations presents substantial obstacles. This system is a paradigm shift in providing safe and independent mobility assistance for the visually handicapped, as it makes use of cutting-edge technologies. The voice navigation system gives users the confidence to confidently navigate uncharted territory by seamlessly integrating computer vision and machine learning capabilities. With the use of real-time obstacle classification and recognition capabilities, the system can give users vital information about their surroundings, empowering them to make wise decisions while traveling. The system's smartphone-based platform, which provides an intuitive interface that can be accessed with voice commands, is its basis. By utilizing deep learning algorithms and pre-trained models, the system can precisely identify barriers and guide people across intricate environments. Precise positioning and route planning are made possible by the system's integration of GPS technology. Users receive up-to-date information on their location and the most efficient route to their destination via voice-activated instructions and advice. Individual user preferences are catered to by customizable options, which guarantee a tailored and user-friendly experience. A wider user base can utilize the system, democratizing access to necessary navigation tools, due to its affordability and interoperability with commonly available smartphone devices. The results of experimental tests highlight the effectiveness of the system, showing that it can reliably and accurately guide visually impaired people across a variety of surroundings. Through the promotion of increased autonomy and mobility, the voice navigation system enables users to move with unprecedented liberty and self-assurance, surmounting obstacles and opening up novel prospects. Key Words: : Voice navigation system, Machine learning, GPS technology, Deep learning algorithms, Navigation tools.

Exploring stakeholders’ perspectives on antibiogram use, development, and implementation in residential aged care settings

BackgroundKnowledge of local antibiotic resistance data provided by antibiograms (cumulative-antimicrobial-susceptibility-tests) can assist prescribers to make appropriate empirical antibiotic choices. ObjectiveThis study explored the perceptions and knowledge of key stakeholders about the role of antibiograms in residential aged care facilities (RACF), and to understand barriers and enablers of antibiogram development and implementation in this setting. MethodSemi-structured interviews were conducted with aged-care health professionals (‘end-users’) and antibiogram content experts. This study was conducted in Queensland, Australia in 2023. Using qualitative techniques, framework thematic analysis was used to identify themes, which were mapped to the ‘Integrated Promoting Action on Research Implementation in Health Services’ framework constructs. ResultsTwenty interviews were conducted comprising of five ‘content-experts’ and fifteen ‘end-users’. Five themes were identified which indicated lack of knowledge about how to use antibiograms, and its availability. Potential insufficient data was the primary issue identified by content experts with regards to feasibility of annual antibiograms. Pragmatic solutions were offered, such as pooling pathology data from facilities in the same geographical location, extending antibiogram data to two-or three-yearly, or utilising local hospital antibiograms. Presenting antibiogram data in a mode and format suiting preferences of individual users would encourage uptake and improve usability. Antimicrobial stewardship (AMS) champions and pharmacists were highlighted as drivers of educating and promoting antibiogram use. ConclusionClinicians recognised the potential role of antibiograms in improving empirical antibiotic prescribing choices. Establishing their baseline knowledge provides an essential starting point for the education needs of this group. This study provides practical recommendations regarding the presentation of antibiograms to ensure appropriate use and uptake as an AMS tool in RACFs. Pragmatic solutions suggested to overcome challenges of antibiogram development for RACFs should be applied and evaluated to determine feasibility of RACF-specific antibiograms.

Enhancing User Experience: Exploring Mobile Augmented Reality Experiences

In recent years, the integration of augmented reality (AR) technology into mobile devices has revolutionized both human-computer interaction and computer graphics. This transformative blend of virtual elements into the physical world has led to a new era of possibilities for user experiences. However, the unique challenges posed by Mobile Augmented Reality (MAR) applications demand a closer examination of design and usability considerations to ensure optimal user engagement and satisfaction. This study investigates users' experiences with Mobile Augmented Reality applications, with particular emphasis on design and usability-related concerns. Using a controlled experiment with twenty different people, this study uses thematic analysis to investigate UX improvement options. The aim is to furnish practical design principles that consider the individual characteristics and preferences of users, thereby contributing to the development of empirical insights that enhance Human-Computer Interaction (HCI) standards and best practices. Moreover, the results highlight the importance of user-centered design and assessment approaches. This work fills important gaps in the literature on UX studies of MAR applications and advances our knowledge of creating, engaging and easy-to-use augmented reality experiences.

ChatDiet: Empowering personalized nutrition-oriented food recommender chatbots through an LLM-augmented framework

The profound impact of food on health necessitates advanced nutrition-oriented food recommendation services. Conventional methods often lack the crucial elements of personalization, explainability, and interactivity. While Large Language Models (LLMs) bring interpretability and explainability, their standalone use falls short of achieving true personalization. In this paper, we introduce ChatDiet, a novel LLM-powered framework designed specifically for personalized nutrition-oriented food recommendation chatbots. ChatDiet integrates personal and population models, complemented by an orchestrator, to seamlessly retrieve and process pertinent information. The personal model leverages causal discovery and inference techniques to assess personalized nutritional effects for a specific user, whereas the population model provides generalized information on food nutritional content. The orchestrator retrieves, synergizes and delivers the output of both models to the LLM, providing tailored food recommendations designed to support targeted health outcomes. The result is a dynamic delivery of personalized and explainable food recommendations, tailored to individual user preferences. Our evaluation of ChatDiet includes a compelling case study, where we establish a causal personal model to estimate individual nutrition effects. Our assessments, including a food recommendation test showcasing a 92% effectiveness rate, coupled with illustrative dialogue examples, underscore ChatDiet’s strengths in explainability, personalization, and interactivity.

AI-driven personalization in web content delivery: A comparative study of user engagement in the USA and the UK

In the ever-evolving landscape of digital experiences, AI-driven personalization has emerged as a pivotal force shaping how users interact with web content. This study conducts a comparative analysis of AI-driven personalization strategies in web content delivery, focusing on user engagement in the United States (USA) and the United Kingdom (UK). The research delves into the nuanced ways in which AI algorithms tailor web content to individual user preferences, examining the impact on user engagement metrics such as time spent on site, click-through rates, and conversion rates. Through a meticulous examination of AI-driven personalization practices employed by web platforms in both regions, this study seeks to identify common trends, regional differentiators, and their implications on user engagement. Key factors considered include the ethical dimensions of personalization, the adaptability of AI algorithms to diverse user behaviors, and the fine balance between customization and privacy concerns. The findings aim to contribute valuable insights to the fields of AI-driven web content delivery, user experience design, and the global discourse on the intersection of technology, personalization, and user engagement.

Shrinking VOD Traffic via Rényi-Entropic Optimal Transport

In response to the exponential surge in Internet Video on Demand (VOD) traffic, numerous research endeavors have concentrated on optimizing and enhancing infrastructure efficiency. In contrast, this paper explores whether users' demand patterns can be shaped to reduce the pressure on infrastructure. Our main idea is to design a mechanism that alters the distribution of user requests to another distribution which is much more cache-efficient, but still remains 'close enough' (in the sense of cost) to fulfil each individual user's preference. To quantify the cache footprint of VOD traffic, we propose a novel application of Rényi entropy as its proxy, capturing the 'richness' (the number of distinct videos or cache size) and the 'evenness' (the relative popularity of video accesses) of the on-demand video distribution. We then demonstrate how to decrease this metric by formulating a problem drawing on the mathematical theory of optimal transport (OT). Additionally, we establish a key equivalence theorem: minimizing Rényi entropy corresponds to maximizing soft cache hit ratio (SCHR) --- a variant of cache hit ratio allowing similarity-based video substitutions. Evaluation on a real-world, city-scale video viewing dataset reveals a remarkable 83% reduction in cache size (associated with VOD caching traffic). Crucially, in alignment with the above-mentioned equivalence theorem, our approach yields a significant uplift to SCHR, achieving close to 100%.

Development of information system for planning personalized tourist routes

The object of research is processes of information and technological planning of tourist trips. The article analyzes the processes of information technology planning of tourist trips and develops an information system for personalized planning of tourist routes using graph theory. The paper describes in detail a new approach to the formation of optimal routes based on graph models and the analysis of personal preferences of users. The developed information system uses graph theory to represent and optimize tourist routes, taking into account various criteria such as distance, travel time, and individual user preferences. Algorithms for calculating optimal routes are designed to improve the quality of recommendations and provide a high level of personalization. The functionality of the information system for planning tourist routes is based on the use of a number of innovative approaches and technologies that make the system more efficient, accurate and attractive to users. This takes into account the need to use modern route optimization algorithms. Algorithms have been developed to optimize tourist routes based on various criteria, such as cost, time, personal preferences, and environmental aspects. The introduction of intelligent analytical tools for predicting tourist trends helps to determine the popularity of places and other factors that influence the choice of routes. For the audio support of the excursion route, an algorithm for generating multimedia information content that is relevant to an individual personalized excursion route and its duration is used. The authors formulate the requirements and analyze the functionality of the information system for personalized planning of tourist routes, since a personalized approach and the use of graph models facilitate route planning that takes into account various aspects, including user-friendliness, accuracy of recommendations, system flexibility and security. The developed information system can open up new opportunities for individual and intelligent planning of tourist routes, providing users with a unique tourist experience.