User Preferences Research Articles

Home Services Recommendation System using Machine Learning for Urban Areas

The Kotlin-based on-demand home service system streamlines household tasks by offering services such as repairs, cleaning, gardening, and plumbing. In today's fast-paced world, it provides quick solutions for maintaining a clean and hygienic living environment. The Housekeeper Finder app is a prominent player in this space, utilizing advanced machine learning algorithms to deliver personalized service recommendations based on user preferences. It includes essential services like cooking, laundry, and elder care, along with a robust feedback system to improve service quality. Users can easily book services from anywhere, leveraging GPS technology to connect them with nearby providers. The "Domestic Android Application for Home Services" further enhances accessibility through location-based service requests and mapping capabilities for a seamless user experience across platforms.A s cities transition into Smart Cities, integrated information systems open new avenues for urban service recommendations, fostering liveability and sustainability. Recommendation systems analyse user behaviour to provide tailored suggestions using techniques like collaborative filtering and hybrid methods. In response to the challenges of modern work culture, the e-commerce landscape now offers easy access to essential services via mobile applications, ultimately transforming how households address their needs and promoting safer, cleaner living space.

Beyond Preferences in AI Alignment

AbstractThe dominant practice of AI alignment assumes (1) that preferences are an adequate representation of human values, (2) that human rationality can be understood in terms of maximizing the satisfaction of preferences, and (3) that AI systems should be aligned with the preferences of one or more humans to ensure that they behave safely and in accordance with our values. Whether implicitly followed or explicitly endorsed, these commitments constitute what we term a preferentist approach to AI alignment. In this paper, we characterize and challenge the preferentist approach, describing conceptual and technical alternatives that are ripe for further research. We first survey the limits of rational choice theory as a descriptive model, explaining how preferences fail to capture the thick semantic content of human values, and how utility representations neglect the possible incommensurability of those values. We then critique the normativity of expected utility theory (EUT) for humans and AI, drawing upon arguments showing how rational agents need not comply with EUT, while highlighting how EUT is silent on which preferences are normatively acceptable. Finally, we argue that these limitations motivate a reframing of the targets of AI alignment: Instead of alignment with the preferences of a human user, developer, or humanity-writ-large, AI systems should be aligned with normative standards appropriate to their social roles, such as the role of a general-purpose assistant. Furthermore, these standards should be negotiated and agreed upon by all relevant stakeholders. On this alternative conception of alignment, a multiplicity of AI systems will be able to serve diverse ends, aligned with normative standards that promote mutual benefit and limit harm despite our plural and divergent values.

Preferences and valuation of small-scale wastewater treatment system maintenance using discrete choice method: Case of community-managed system in Jakarta, Indonesia

The focus of public service delivery on sustainability and universal access has enabled the enactment of many community-based programs in Indonesia. However, relevant studies that examine the willingness to pay among households that utilize community-managed facilities are lacking, despite it being one of the most important factors for maintenance and a strong indicator of commitment. In the present study, we aimed to estimate household users' preferences and willingness to pay (WTP) to understand their demand for related services. It estimates household user preferences and willingness to pay to understand the demand for related services through a choice experiment. By comparing all service attributes, the results highlight that users derive the highest utility if they updated reactors and pipelines, and, conversely, if the responsible authority is changed. The desire for quality improvement and service expansion were confirmed using this model. The mixed model estimated a willingness to pay of 0.03–3.13 USD for average users, depending on the attributes. Our findings confirm that families who belong to a community that operates a decentralized system derive greater benefits from improved maintenance; thus, they are willing to pay more to enjoy the service. Notably, at its core, a community-managed system, such as the Sanimas Program, is a co-production initiative between multiple levels of government institutions and local residents. Therefore, maintenance actions should involve resource- and payment-intensive efforts, such as half-year sanitary desludging and system replacement owing to deterioration and hazards. Policy regulations that correct the distribution of roles among partners for maintaining the system should be initiated. These findings provide evidence for the government's commitment to the community and could help convince other stakeholders to redirect their attention to supporting sustainable system performance.

Healthcare professionals’ preferences for the specialist pharmacy service medicines advice clinical enquiry service: a discrete choice experiment

Abstract Introduction The Specialist Pharmacy Service Medicines Advice (SPS-MA) service answers enquiries from primary care professionals in England. The preferences of service users for the way this service is provided (e.g. opening hours, speed and route of reply, amount of detail in answer etc.) are unknown. Such information would be useful to ensure the service meets the needs of users as well as possible within the constraints of available resources. Aim To determine healthcare professionals’ preferences for the Specialist Pharmacy Service Medicines Advice clinical enquiry service using a discrete choice experiment (DCE). Methods International Society for Pharmacoeconomics and Outcomes Research good research practices were used to conduct a DCE1. Previous literature2, documentary analysis of SPS-MA policies, and interviews with four SPS-MA enquirers and three SPS-MA service providers/managers identified seven key attributes of the clinical enquiry service. Each attribute could be defined using one of 2-3 descriptions (‘levels’). A D-efficient experimental design generated 20 pairs of potential clinical enquiry service variants, each described by one level linked to each of the seven attributes. These 20 pairs were blocked into two online surveys (10 pairs per survey), which asked participants to choose which service from each pair they would prefer to contact. The 1669 SPS-MA clinical enquiry contacts with a recorded email in the year to November 2023 were randomised to receive an email invitation to complete one of the survey variants. Preferences for each attribute level were estimated using conditional logit regression. Using the HRA decision tool, this project was not considered research by the NHS. It was given a favourable opinion by the University of Bath ethics review process on 27th November 2023 (reference 1153-2222). Results 236 participants completed a survey (52% GP pharmacists, 26% other pharmacists, 22% other professional; median 15 years’ experience). Weekend opening and opening hours did not significantly affect preferences (p≥0.226). All other attributes did significantly affect preferences (p≤0.015). An answer within one hour or two days was most valued (preference weightings (95%CI) relative to five days: 0.89 (0.76-1.03) and 0.58 (0.45-0.72), respectively). Communication by email was highly valued (preference weighting: 0.62 (0.47-0.77) relative to telephone only). The availability of a website of frequently asked questions was also valued (preference weighting: 0.48 (0.31-0.64) relative to no website). Answering the telephone within 20 seconds and a detailed response were valued, but to a lesser degree. Sub-group analysis found that participants qualified for <15 years valued email communication and detailed responses more strongly than other participants (p≤0.035). Discussion / Conclusion Where clinically appropriate, the SPS-MA clinical enquiry service should prioritise speed of reply, communication by email and a website of frequently asked questions. Longer opening hours are not required. This is the first study to use of a DCE to investigate professional enquirers’ preferences for a medicines information/advice service. This allowed analysis of the strength of preferences and how they were traded off. Due to the small number of non-pharmacist enquirers to the SPS-MA service, it was not possible to investigate how professional background influences preferences.

Knowledge maps as a complementary tool to learn and teach surgical anatomy in virtual reality: A case study in dental implantology

AbstractA thorough understanding of surgical anatomy is essential for preparing and training medical students to become competent and skilled surgeons. While Virtual Reality (VR) has shown to be a suitable interaction paradigm for surgical training, traditional anatomical VR models often rely on simple labels and arrows pointing to relevant landmarks. Yet, studies have indicated that such visual settings could benefit from knowledge maps as such representations explicitly illustrate the conceptual connections between anatomical landmarks. In this article, a VR educational tool is presented designed to explore the potential of knowledge maps as a complementary visual encoding for labeled 3D anatomy models. Focusing on surgical anatomy for implantology, it was investigated whether integrating knowledge maps within a VR environment could improve students' understanding and retention of complex anatomical relationships. The study involved 30 master's students in dentistry and 3 anatomy teachers, who used the tool and were subsequently assessed through surgical anatomy quizzes (measuring both completion times and scores) and subjective feedback (assessing user satisfaction, preferences, system usability, and task workload). The results showed that using knowledge maps in an immersive environment facilitates learning and teaching surgical anatomy applied to implantology, serving as a complementary tool to conventional VR educational methods.

Exploring the Influence of Syntactic Structures on English Syntactic Priming

Abstract: English syntactic structures are essential components in the process of learning English as a second language (ESL) and play a critical role in the acquisition process. Based on previous research, this paper discusses the impact of syntactic structures on English L2 syntactic priming among Chinese learners from three perspectives: research paradigms, priming structures, and the factors influencing priming effects. The findings indicate that, given syntactic structure interchangeability, different syntactic structures exhibit varying degrees of priming effects. Key factors such as L2 proficiency, syntactic usage preferences, and language structureincluding both structural differences within English and similarities between the learner's native language and Englishsignificantly affect the extent of these priming effects. These insights not only contribute to further research on English L2 syntactic priming but also provide practical guidance for ESL teaching, highlighting the importance of understanding these dynamics to improve language acquisition and instructional strategies. It also enables English as a second language learners to fully understand the mechanisms of syntactic priming effects, providing them with scientific learning methods, thereby allowing them to learn English more efficiently.

A Heterogeneous Multi-Satellite Dynamic Mission Planning Method Based on Metaheuristic Algorithms

The increasing demand for satellite communication necessitates efficient resource management, especially as next-generation high-throughput satellites (HTSs) face challenges in optimizing user connections. This paper presents a novel method that integrates a discretely improved Particle Swarm Optimization (PSO) algorithm with a dynamic task management framework to enhance satellite resource allocation efficiency. The PSO algorithm is adapted for discrete selection problems to maximize a fitness function based on user priority, user preference, and capacity satisfaction, thus ensuring accurate user–satellite matching. A main loop function iteratively updates user data and connection statuses, thus achieving continuous optimization of satellite connections. Through simulation studies, we validated the effectiveness of this method under dynamically changing user demands, demonstrating that the Discrete PSO algorithm significantly outperforms Simulated Annealing (SA) and the Genetic Algorithm (GA) in user satisfaction, maintaining levels above 0.996 even under high demand. Additionally, it effectively prioritizes high-demand users, ensuring their satisfaction remains above 0.95. Overall, our method enhances the management of daily communication tasks, significantly improving service quality and user satisfaction.

UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation

To tackle the challenges of cold start and data sparsity in recommendation systems, an increasing number of researchers are integrating item features, resulting in the emergence of multimodal recommendation systems. Although graph convolutional network-based approaches have achieved significant success, they still face two limitations: (1) Users have different preferences for various types of features, but existing methods often treat these preferences equally or fail to specifically address this issue. (2) They do not effectively distinguish the similarity between different modality item features, overlook the unique characteristics of each type, and fail to fully exploit their complementarity. To solve these issues, we propose the user perception-guided graph convolutional network for multimodal recommendation (UPGCN). This model consists of two main parts: the user perception-guided representation enhancement module (UPEM) and the multimodal two-step enhanced fusion method, which are designed to capture user preferences for different modalities to enhance user representation. At the same time, by distinguishing the similarity between different modalities, the model filters out noise and fully leverages their complementarity to achieve more accurate item representations. We performed comprehensive experiments on the proposed model, and the results indicate that it outperforms other baseline models in recommendation performance, strongly demonstrating its effectiveness.

Splits Revolutionizing Access to Affordable Streaming Subscriptions

Charges of subscriptions in the growing competition of online entertainment are sometimes very much impacting the customer’s pocket. To overcome this challenge, there is Splits that provides an opportunity to subscribe for numerous streaming services with better conditions than individual companies; streaming services include Netflix, Prime Video, Disney+, SonyLiv, YouTube Premium, Spotify, etc. In this paper, the role and efficiency of Splits in letting the users subscribe to group plans and allowing all of them to access important content while still decreasing the cost dramatically will be discussed. The completely constructed application named Splits allows users to quickly create groups and enjoy their favorite services multiple times cheaper than usual thanks to a comfortable interface. Initial results reveal the factors in health care cost saving with high levels of user satisfaction and participation. In addition, the paper examines possible future developments of the model such as the addition of user preferences and incorporation of feedback for tailored streaming services. Splits help this study, as it presents an insight into the estimates of economic effects of Splits on subscription-based entertainment; thus, enlarging the comprehension of the changing consumer access to digital media and improving the practical recommendations on individual decision- making in an ever more digitalised world. INDEX TERMS : Splits, Streaming Services, Subscription Model, Cost-Effective Entertainment, Group Subscriptions, Digital Media Access, Consumer .

A generative and discriminative model for diversity-promoting recommendation

Diversity-promoting recommender systems with the goal of recommending diverse and relevant results to users, have received significant attention. However, current studies often face a trade-off: they either recommend highly accurate but homogeneous items or boost diversity at the cost of relevance, making it challenging for users to find truly satisfying recommendations that meet both their obvious and potential needs. To overcome this competitive trade-off, we introduce a unified framework that simultaneously leverages a discriminative model and a generative model. This approach allows us to adjust the focus of learning dynamically. Specifically, our framework uses Variational Graph Auto-Encoders to enhance the diversity of recommendations, while Graph Convolution Networks are employed to ensure high accuracy in predicting user preferences. This dual focus enables our system to deliver recommendations that are both diverse and closely aligned with user interests. Inspired by the quality vs. diversity decomposition of Determinantal Point Process (DPP) kernel, we design the DPP likelihood-based loss function as the joint modeling loss. Extensive experiments on three real-world datasets, demonstrating that the unified framework goes beyond quality-diversity trade-off, i.e., instead of sacrificing accuracy for promoting diversity, the joint modeling actually boosts both metrics.

Persuasive software features as antecedents to perceived compliance within physical activity behavior change support systems

ABSTRACT Although behavior change support systems (BCSS) have the potential to improve compliance using persuasive software features, there is a dearth of empirical evidence on how these features impact perceived compliance. This study aimed to investigate how and to what extent the persuasiv systems design (PSD) principles in physical activity BCSS influence perceived compliance. Quantitative data from 403 participants (165 users and 238 prospective users of BCSS designed for PA) was analyzed using partial least squares structural equation modeling. Results revealed differences in how the PSD principles impact perceived compliance between users and prospective users. Perceptions of dialogue support, credibility support, and social support were more relevant to prospective users than to users. Thus, designers should pay more attention to these principles to attract and engage prospective users of BCSS. Conversely, primary task support was more relevant to users than to prospective users. This indicates that once prospective users transition to active users, the focus should shift towards providing persuasive features that facilitate the performance of the primary task. These findings underscore the importance of developing intelligent BCSS that can adapt to the evolving needs and preferences of users at different stages of their interactions with a system.

User engagement in digital curation: A systematic review and synthesis

This review article synthesizes current research findings to propose effective strategies for measuring and enhancing user engagement in digital curation. This review article synthesizes current research findings to propose effective strategies for measuring and enhancing user engagement in digital curation. By establishing comprehensive frameworks and methodologies, researchers and practitioners can gain deeper insights into user behaviors, preferences, and interactions with curated content. This understanding will improve digital curation practices, ensuring curated resources meet user expectations and needs. The review identifies best practices, offers actionable recommendations, and highlights the critical need for robust user engagement strategies. Through systematic analysis of existing literature, it empowers the digital curation community to develop more effective strategies for managing and curating digital resources, ultimately enhancing user experience and engagement across diverse digital platforms.

Warm for fun, cool for work: the effect of color temperature on users’ attitudes and behaviors toward hedonic vs. utilitarian mobile apps

PurposeIn the competitive app store environment, understanding how various app store elements influence user preferences is crucial. While prior research has primarily focused on textual and numeric elements of app stores, the role of visual elements remains underexplored. This study addresses this gap by investigating how color temperature (warm vs. cool) in app screenshots can influence users’ intentions to download hedonic vs. utilitarian mobile apps.Design/methodology/approachMultiple online experiments were conducted, including two main experiments (N = 192) and two supplementary experiments (N = 157). The experiments manipulated the color temperature of screenshots for hedonic (e.g. gaming) and utilitarian (e.g. banking) apps and measured participants’ perceptions of color appropriateness, attitudinal responses, and download intentions.FindingsWarm colors were perceived as more appropriate for hedonic apps, while cool colors were perceived as more suitable for utilitarian apps. The congruence between color temperature and app value orientation significantly influenced users’ ad attitudes and app download intentions. Specifically, hedonic apps in warm colors and utilitarian apps in cool colors received more favorable responses, with perceived color appropriateness mediating this effect.Originality/valueThis research enhances the understanding of the impact of color on app users’ perceptions and behaviors and also offers valuable insights for app developers and marketers on effectively using color as a visual marketing tool in app store environments.

Ergonomics in Bicycle Saddle Design: Application of TRIZ Innovation System Method with IPA-Kano Model Validation

This study investigates the innovative design of a bicycle saddle by incorporating sustainable ergonomics, universal design principles, and systematic innovation methods. Initially, the literature related to bicycle saddle design and its impact on the human body during riding was analyzed. The TRIZ contradiction matrix was then used to identify relevant invention principles, which served as references for the innovative design of the bicycle saddle. Biomechanics and the human–machine system analysis within human factors engineering were applied to ensure the innovative design is ergonomic and user-friendly. The design features a horizontally expandable and foldable bicycle saddle, enhancing its adaptability and sustainability. Universal design principles were applied to make the innovative design more accessible to the general public, and the prototype was simulated using Inventor drawing software. The research results include: (1) An innovative bicycle saddle design with horizontal expansion and folding functions is proposed. This design divides the saddle into three components, enabling the left and right parts to expand or retract based on user preferences. (2) A bicycle backrest design featuring vertical adjustability is introduced. It incorporates a quick-release adjustment mechanism at the junction of the backrest and saddle, allowing users to freely adjust the backrest height. (3) A quick-operation bicycle saddle design is presented, utilizing quick-release screws to facilitate the swift operation of the horizontal expansion and folding mechanisms. This validation method confirmed that the innovative design meets both sustainable ergonomic standards and user expectations. The systematic innovation approach used in this study can serve as a valuable reference for future research and design applications.

Electric Vehicle Charging Station Recommendations Considering User Charging Preferences Based on Comment Data

User preferences are important for electric vehicle charging station (EVCS) recommendations, but they have not been deeply analyzed. Therefore, in this study, user charging preferences are identified and applied to EVCS recommendations using a hybrid model that integrates LightGBM and singular value decomposition (SVD). In the model, LightGBM is used to predict user ratings according to users’ comments regarding charging orders, and the feature importance reported by each user is output. Then, a co-occurrence matrix between users and charging stations (EVCSs) is constructed and decomposed using SVD. Based on the decomposed results, the final evaluated scores of each user for EVCSs can be calculated. Upon ranking the EVCSs according to the scores, the EVCS recommendation results are obtained, taking into account the users’ charging preferences. The sample data consist of 28,306 orders from 508 users at 241 charging stations in Linyi, Shandong, China. The experimental results show that the proposed hybrid model outperforms the benchmark models in terms of precision, recall, and F1 score, and its F1 score can be increased by 96% compared with that of the traditional item-based collaborative filtering method with charging counts for EVCS recommendations.

Cloud Platform Business Scenario Modeling Based on Multi-Model Fusion Recommendation Algorithm

In the era of cloud computing, businesses are increasingly relying on cloud platforms to streamline their operations and deliver services efficiently. Recommendation systems play a pivotal role in suggesting suitable services and resources to enhance user experience and optimize resource allocation. This paper presents a novel approach, the Multi-Model Fusion Recommendation Algorithm (MMFRA), which integrates multiple recommendation models using advanced fusion techniques to enhance the accuracy of recommendations in cloud platform business scenarios. The implementation process of MMFRA involves combining diverse recommendation models, such as collaborative filtering, content-based filtering, and matrix factorization, into a unified framework that leverages their strengths while mitigating individual limitations. This fusion process is designed to achieve higher precision in service recommendations by considering various aspects of user behavior and preferences. Through a comprehensive evaluation in a simulated cloud platform environment, MMFRA demonstrates superior performance in terms of recommendation accuracy and user satisfaction. The proposed algorithm offers significant potential for enhancing the effectiveness of cloud platform services, ultimately benefiting both service providers and users.

Adaptive 360° video timeline exploration in VR environment

Timeline control is a crucial interaction during video viewing, aiding users in quickly locating or jumping to specific points in the video playback, especially when dealing with lengthy content. 360°videos, with their ability to offer an all-encompassing view, have gradually gained popularity, providing a more immersive experience compared to videos with a single perspective. While most 360°videos are currently displayed on two-dimensional screens, the timeline design has largely remained similar to that of conventional videos. However, virtual reality (VR) headsets provide a more immersive viewing experience for 360°videos and offer additional dimensions for timeline design. In this paper, we initially explored 6 timeline design styles by varying the shape and interaction distance of the timeline, aiming to discover designs more suitable for the VR environment of 360°videos. Subsequently, we introduced an adaptive timeline display mechanism based on eye gaze sequences to optimize the timeline, addressing issues like obstructing the view and causing distractions when the timeline is consistently visible. Through two studies, we first demonstrated that in the 360°space, the three-dimensional timeline performs better in terms of usability than the two-dimensional one, and the reachable timeline has advantages in performance and experience over the distant one. Secondly, we verified that, without compromising interaction efficiency and system usability, the adaptive display timeline gained more user preference due to its accurate prediction of user timeline needs.

A mixed-methods survey and focus group study to understand researcher and clinician preferences for a Journal transparency Tool

Transparency within biomedical research is essential for research integrity, credibility, and reproducibility. To increase adherence to optimal scientific practices and enhance transparency, we propose the creation of a journal transparency tool (JTT) that will allow users to obtain information about a given scholarly journal’s operations and transparency policies. This study is part of a program of research to obtain user preferences to inform the proposed JTT. Here, we report on our consultation with clinicians and researchers. This mixed-methods study was conducted in two parts. The first part involved a cross-sectional survey conducted on a random sample of authors from biomedical journals. The survey asked clinicians and researchers about the inclusion of a series of potential scholarly metrics and user features in the proposed JTT. Quantitative survey items were summarized with descriptive statistics. Thematic content analysis was employed to analyze text-based responses. Subsequent focus groups used survey responses to further explore the inclusion of items in the JTT. Items with less than 70% agreement were used to structure discussion points during these sessions. Participants voted on the use of user features and metrics to be considered within the journal tool after each discussion. Thematic content analysis was conducted on interview transcripts to identify the core themes discussed. A total of 632 participants (5.5% response rate) took part in the survey. A collective total of 74.7% of respondents found it either ‘occasionally, ‘often’, or ‘almost always’ difficult to determine if health information online is based on reliable research evidence. Twenty-two participants took part in the focus groups. Three user features and five journal tool metrics were major discussion points during these sessions. Thematic analysis of interview transcripts resulted in six themes. The use of registration was the only item to not meet the 70% threshold after both the survey and focus groups. Participants demonstrated low scholarly communication literacy when discussing tool metric suggestions. Our findings suggest that the JTT would be valuable for both researchers and clinicians. The outcomes of this research will contribute to developing and refining the tool in accordance with researchers and clinicians.

Demand management of plug-in electric vehicle charging station considering bidirectional power flow using deep reinforcement learning

The recent development in plug-in electric vehicle technology has increased its popularity. Plug-in electric vehicles are widely used for their environment-friendly nature and they contribute to the reduction in global warming. With the increasing number of plug-in electric vehicles, charging coordination becomes essential for managing the charging station demand. The random charging behaviour of plug-in electric vehicles makes it a difficult task. This paper proposes a novel demand management strategy of charging stations. The proposed strategy supports the grid and reduces its burden during peak load. Deep-Q network based deep reinforcement learning is used in the proposed strategy. It is a value based deep reinforcement learning algorithm that approximates the Q value function using deep neural network. The deep reinforcement schedules the charging and discharging of plug-in electric vehicles to optimize the cost and manage the charging station load. Deep reinforcement learning enhances charging coordination by dynamically optimizing charging schedules according to real-time conditions and user preferences, thereby increasing efficiency and better integration with the grid. The reward function in deep reinforcement learning is designed based on the power price and demand of the charging station. A discount factor is introduced based on the service time to make the charging coordination efficient. A case study using dynamic pricing is carried out to validate the proposed strategy. The results prove that the proposed strategy optimizes charging and discharging costs and manages charging station demand efficiently. It is also observed that the proposed strategy is fast and incurs less computational cost.

Visual Perceptual Confidence: Exploring Discrepancies Between Self-reported and Actual Distance Perception In Virtual Reality.

Virtual Reality (VR) systems are widely used, and it is essential to know if spatial perception in virtual environments (VEs) is similar to reality. Research indicates that users tend to underestimate distances in VR. Prior work suggests that actual distance judgments in VR may not always match the users self-reported preference of where they think they most accurately estimated distances. However, no explicit investigation evaluated whether user preferences match actual performance in a spatial judgment task. We used blind walking to explore potential dissimilarities between actual distance estimates and user-selected preferences of visual complexities, VE conditions, and targets. Our findings show a gap between user preferences and actual performance when visual complexities were varied, which has implications for better visual perception understanding, VR applications design, and research in spatial perception, indicating the need to calibrate and align user preferences and true spatial perception abilities in VR.