Satisfactory User Experience Research Articles

A hierarchical and multi-view registration of serial histopathological images

Serial histopathological images sliced from the same biopsy section exhibit quite similar topological structures. They are usually stained with different dyes and inspected individually for different clinical purposes in the routine workflow of pathology. Recently there is increasing demand for browsing them jointly, e.g., viewing the colocalization of biomarkers. Image registration is recognized as a feasible means to bridge this requirement. However, it is challenging to register the images due to their natural content difference, variation and distortion introduced from slice preparation, the extremely large image size, etc. This letter proposes a novel Hierarchical and Multi-View Registration (HMVReg) method to alleviate the difficulties. It is a three-stage coarse-to-fine registration consisting of global rigid registration, multi-view affine transformation and multi-view elastic registration, respectively for eliminating the translational and rotational differences, globally isotropic deformation and locally anisotropic distortion. The stages are complementary and jointly contribute to a stable registration. Experimental results on both private and public data, which cover histopathological images from multiple organ tissues, demonstrate the effectiveness of the proposed HMVReg. It not only well balance the registration accuracy, robustness and speed, where serial but differently stained images are accurately aligned in a reasonable time cost. Moreover, it also shows satisfactory user experience in applying the alignment to biomarker colocalization browsing.

Assessing Video Game Satisfaction of Gamers with Disabilities

According to a survey conducted by the CDC, it is estimated that 26% of Americans are living with a disability. Of those with disabilities in the United States, it is estimated that 33 million play video games. People with disabilities face many barriers in gaming, which is likely to impact game satisfaction. Measuring game satisfaction among this population can be problematic if the scale is not adapted to their needs, which can vary significantly based on the disability. To understand how best to assess game satisfaction among these populations, we discuss the issues people with cognitive, sensory, and/or motor disabilities may face when completing assessment scales and then use the validated Game User Experience Satisfaction Scale (GUESS) as a framework for understanding the game design issues that may impact satisfaction.

Analysis of priority capture method for dynamic requirements of intelligent products based on user scenarios

To solve the problem of the fuzzy and dynamics of requirement caused by users’ cognitive bias, a dynamic requirement and priority capture method based on user scenarios is proposed, aiming at effectively improving user experience. The method consists of the following steps: Firstly, users with similar characteristics are filtered to form a user cluster, then obtain the user’s product experience in different usage scenarios and acquire preliminary requirements by using service design methods. Secondly, the requirement path model tree will be designed and the requirement path matrix will be constructed through the evaluation of the user cluster. Then the pathfinder algorithm will be used to calculate the required correlation of user clusters and prioritize the requirements. Finally, the direction of the product design will be provided. Taking the design of the intelligent office chair as an example, the effectiveness of the method is verified by evaluating the satisfaction of user experience.

A Novel Approach for Developing Web Blog APP

The main aim of this project is to provide data to students in only one site. Students can gather the information from one site as well as give their feedback and create their own blog. Students can post their views and thought and analyse themselves. Besides all such core functionalities, the application also includes features like FAQ, request, feedback etc. so as to provide a satisfactory user experience. The homepage will list all blog posts, and there will be a dedicated detail page for each individual post. A blog is a great addition to any personal portfolio site. Whether you update it monthly or weekly, it’s a great place to share your knowledge as you learn. If user wants to access database and want to post a blog they have to sign in that they have created. In this section, you’re going to build a fully functioning blog that will allow you to perform the following tasks like create, update, and delete blog posts, display posts to the user as either an index view or a detail view and assign categories to posts and allow users to comment on posts.

Progressive Traffic-Oriented Resource Management for Reducing Network Congestion in Edge Computing.

Edge computing can deliver network services with low latency and real-time processing by providing cloud services at the network edge. Edge computing has a number of advantages such as low latency, locality, and network traffic distribution, but the associated resource management has become a significant challenge because of its inherent hierarchical, distributed, and heterogeneous nature. Various cloud-based network services such as crowd sensing, hierarchical deep learning systems, and cloud gaming each have their own traffic patterns and computing requirements. To provide a satisfactory user experience for these services, resource management that comprehensively considers service diversity, client usage patterns, and network performance indicators is required. In this study, an algorithm that simultaneously considers computing resources and network traffic load when deploying servers that provide edge services is proposed. The proposed algorithm generates candidate deployments based on factors that affect traffic load, such as the number of servers, server location, and client mapping according to service characteristics and usage. A final deployment plan is then established using a partial vector bin packing scheme that considers both the generated traffic and computing resources in the network. The proposed algorithm is evaluated using several simulations that consider actual network service and device characteristics.

Reinforcement Learning-Based Resource Partitioning for Improving Responsiveness in Cloud Gaming

Cloud gaming has been very popular in recent years, but issues relating to maintaining low interaction delay to guarantee satisfactory user experience are still prevalent. We observe that the server-side processing delay in cloud gaming system could be heavily influenced by how the resources are partitioned among processes. However, finding the optimal partitioning policy that minimizes the response delay faces several critical challenges. In this paper, we propose an online resource partitioning framework for reducing response delay in cloud gaming, which has several promising properties. First, we divide the processes into disjoint groups and partition resources among process groups, which greatly simplifies the resource partitioning problem while ensuring high partitioning effectiveness. Second, to tackle dynamic workload changes, we classify game workloads into several clusters and maintain separate process grouping plan for each cluster. Third, we leverage reinforcement learning to adaptively choose the best actions for minimizing response delay in real time. We evaluate the proposed framework in a real cloud gaming environment using several real games. The experimental results show that our approach can reduce the response delay by 22\% to 41\% compared to a system without resource partitioning, and outperforms other resource partitioning policies significantly.

Improving Accessibility in Online Education: Comparative Analysis of Attitudes of Blind and Deaf Students Toward an Adapted Learning Platform

People with different capacities, such as the deaf and blind, have problems accessing educational content due to lack of accessible technology. Accessibility and usability are closely related concepts that share the goals for a satisfactory user experience. Existing literature establishes a direct relation between accessibility and usability, and reports that there are problems with both in learning platforms, and more generally with most websites. The objective of this paper is to evaluate the accessibility and usability of a learning platform by interrogating its participants. Three groups of students with different capacities (blind, deaf and deaf-blind) used an accessible learning platform prototype to assess the accessibility and usability of the platform and its contents. This article presents a comparative study of the perception and attitude of blind and deaf students towards the use of a learning platform adapted to their personal needs. Results showed that their attitude to the adaptation was very positive but there were differences in the perception of the ease of use of the application and with the level of difficulty to access the learning content. This work contributes to the body of knowledge by showing the effects that adaptations have on learning contents for blind and deaf students in terms of accessibility and ease of use through the analysis of the perceptions of participants. Future work may consider increasing the sample of students, as well as developing and testing new technologies and approaches that address other forms of functional diversity.

Entertainment Chatbot for the Digital Inclusion of Elderly People Without Abstraction Capabilities

Current language processing technologies allow the creation of conversational chatbot platforms. Even though artificial intelligence is still too immature to support satisfactory user experience in many mass market domains, conversational interfaces have found their way into ad hoc applications such as call centres and online shopping assistants. However, they have not been applied so far to social inclusion of elderly people, who are particularly vulnerable to the digital divide. Many of them relieve their loneliness with traditional media such as TV and radio, which are known to create a feeling of companionship. In this paper we present the EBER chatbot, designed to reduce the digital gap for the elderly. EBER reads news in the background and adapts its responses to the user’s mood. Its novelty lies in the concept of “intelligent radio”, according to which, instead of simplifying a digital information system to make it accessible to the elderly, a traditional channel they find familiar -background news- is augmented with interactions via voice dialogues. We make it possible by combining Artificial Intelligence Modelling Language, automatic Natural Language Generation and Sentiment Analysis. The system allows accessing digital content of interest by combining words extracted from user answers to chatbot questions with keywords extracted from the news items. This approach permits defining metrics of the abstraction capabilities of the users depending on a spatial representation of the word space. To prove the suitability of the proposed solution we present results of real experiments conducted with elderly people that provided valuable insights. Our approach was considered satisfactory during the tests and improved the information search capabilities of the participants.

Design of Intelligent Warehouse Management System Based on MVC

Abstract Aiming at the problems of single function, low service level and difficult to meet customers’ demand for warehousing business in warehouse management system, a collaborative filtering recommendation algorithm is proposed. The algorithm calculates the similarity between items by analyzing user’s consumption behavior records, finds similar items to ‘target user’s favorite items, recommends similar items to ‘target user’, and designs commodity recommendation function for customer preferences, so as to improve user satisfaction and commodity sales efficiency. In order to provide users with better service experience and make the system operators easy to use, it is proposed to use B / S architecture and MVC framework model to design four modules : user management, allocation management, warehouse information management and report management, and to establish the corresponding database for unified management. Finally, the intelligent warehouse management system with high user experience satisfaction and convenient background management operation is obtained.

Comparison of VR and Desktop Game User Experience in a Puzzle Game: “Keep Talking and Nobody Explodes”

Since the contemporary game production process is based on the Integrated Development Environment (IDE) applications, it is easier for developers to create multiple versions of their game for both VR and desktop platforms. This provided a great opportunity for researchers to conduct comparative studies to explore the user experience of the relatively novel virtual reality applications In this study, we evaluated a puzzle game through a within-subjects experiment design using objective measures of game success and gameplay duration, as well as plenty of subjective measures in order to assess game user experience, comparing desktop and VR. In addition to selected dimensions of GUESS (Game User Experience Satisfaction Scale), we employed MEC-SPQ (Measurement Effects Conditions - Spatial Presence Questionnaire) to measure presence. Furthermore, we employed NASA-TLX (NASA Task Load Index) to compare the perceived task complexity of the same task executed in VR and desktop gaming environments. Results revealed that there is not a significant difference in objective measures of player performance, comparing the VR and desktop gameplay. The Game User Experience Satisfaction Scale did not reveal any significant difference between the mean scores of VR and desktop experiences. The spatial presence related dimensions of MEC-SPQ revealed significantly higher scores of VR, for Possible Actions and Self Location dimensions. NASA-TLX weighted scores were significantly higher for VR in physical load and for desktop in frustration. Our results show that a puzzle-based game experienced in VR does not lead to a higher level of satisfaction in terms of game user experience but triggers a sense of spatial presence. Due to the different control schemes, players perceive that HMD based gameplay demands more physical task load. However, the gameplay duration and game success rate are not significantly different. The failure in desktop gameplay might have led to higher frustration, since the experience seems more familiar to players. Since the results are partially concordant with previous studies, it is not possible to make a strict conclusion on the effect caused by different immersive technologies on game user experience. Further studies are required through a more consistent methodology with a focus on game design components rather than game genre.

A Virtual Reality Platform for Safety Training in Coal Mines with AI and Cloud Computing

Coal mining, regarded as a high-risk industry, has a strong demand for virtual reality (VR) to fulfill safety and emergency rescue training. In the past ten years, VR technology has significantly improved miner training on both the hardware and software side. However, it still has some drawbacks, such as expensive and unsuitable hardware, lack of satisfactory user experience, without direct browser access, and lack of humanized and intelligent design. To solve these problems, a cloud-based VR system is designed for the training of coal miners in this paper. The system, with browser/client architecture, includes eight modules demonstrating the full procedure of an underground coal mine. The online cloud-rendered video streaming is adopted to provide enough computing and rendering power and hence a better browser-based user experience. Furthermore, game artificial intelligence (AI) is also introduced into the system to increase the emotional exchange between the system and users. Unlike traditional VR training software, this system designs two virtual miners to enhance the experience of trainees. The first virtual miner is a task-oriented non-player-character (NPC) which conveys general knowledge about the mine and guides the users in visiting the underground work sites. The second virtual miner is a disaster-oriented character which prepares the users for typical disasters. The system has been successfully implemented in a laboratory environment, and its performance has been validated. Yet, further practices are needed to stimulate more innovative applications of VR-based miner training and disaster drilling.

A study on User experience Satisfaction Factors of Subscription Service

본 연구는 구독 서비스 중 ‘무제한형 구독 서비스’의 사용자 경험 만족도 요인을 측정하고, 경제적 문화적 관점을 토대로 분석하는 데 목적이 있다. 해당 서비스를 사용해본 경험이 있는 20-30대를 모집하였고, 피터 모빌(Peter Morville)의 허니콤 모형을 재구성하여 작성한 질문을 통해 설문조사를 시행하였다. 설문 결과, 경제적 관점에서는 서비 스 가격과 이용 시간에 대한 가성비 및 구독 유입의 용이성이 만족도 주요 요인이었으며, 문화적 관점에서는 서비스에 대한 친근감과 믿음 및 주변 사람들과의 콘텐츠 공유 경험이 주요 만족 요인이었다. 따라서 구독 서비스의 사용자 경험 만족도는 일차적으로 경제적 만족도가 먼저 충족되어야 문화적 만족도 충족으로도 이어진다는 것을 추론할 수 있다. 본 연구는 무제한형 구독 서비스의 사용자 경험이나 만족도 요인에 관한 연구의 자료로써 활용되기를 기대한다.

E-Guess: Usability Evaluation for Educational Games

Usabilidad es un aspecto relevante en el análisis de la interfaz hombre-máquina, ya que se trata del diálogo que se establece entre sujetos, artefacto y calidad de uso e interacción que permite el sistema. Este trabajo presenta una heurística específica para la evaluación de juegos educativos, creada a partir de Game User Experience Satisfaction (GUESS) y, concomitantemente, las herramientas de evaluación de Nielsen. Para ello, se realizó una investigación aplicada con un enfoque cuantitativo-cualitativo - con la participación de 3 usuarios especializados y 4 usuarios potenciales en un juego educativo utilizado como estudio de caso. La elección de GUESS como punto de partida se debió a una revisión sistemática de la literatura sobre usabilidad. Según el modelo, se invitó a los usuarios a operar el juego educativo y presentar sus impresiones. A partir de los resultados seleccionados, se formuló una nueva herramienta de evaluación de uso, denominada E-GUESS. Con Educational-GUESS, introdujimos cambios dirigidos a temas pedagógicos y contenidos educativos que también busca dilucidar puntos importantes en el desarrollo de un juego educativo al permitir percepciones que son fácilmente ignorados durante la fase de diseño con la intención de superar la supuesta bipolaridad entre "divertido" y "educativo" en los juegos de software educativo. Otro aporte de la investigación fue el análisis de usabilidad realizado para el juego educativo utilizado en la recolección de datos. Este juego, que trata sobre el tema Tabla Periódica de Química y se encuentra en fase de validación, recibió valiosos aportes para ajustes en su jugabilidad.

Relationship Between Children’s Enjoyment, User Experience Satisfaction, and Learning in a Serious Video Game for Nutrition Education: Empirical Pilot Study

BackgroundThe design and use of serious video games for children have increased in recent years. To maximize the effects of these games, it is essential to understand the children’s experiences through playing. Previous studies identified that enjoyment and user experience satisfaction of the players are principal factors that can influence the success of serious video games and the learning of their players. However, research about the relationship between enjoyment and user experience satisfaction with learning in children 8 to 10 years old is sparse.ObjectiveWe examined the relationship of enjoyment and user experience satisfaction with the learning of children aged 8 to 10 years while playing a serious video game for health, FoodRateMaster. This serious video game teaches children about the characteristics of healthy and unhealthy foods and how to identify them in their environment.MethodsChildren aged 8 to 10 years were recruited from a primary school in Mexico. Participants completed 12 individual gaming sessions with FoodRateMaster in 6 weeks. A food knowledge questionnaire was administered before and after game play to assess the players’ food knowledge. In addition, after the gaming sessions, the children’s enjoyment and user experience satisfaction were evaluated using the EGameFlow questionnaire and the Game User Experience Satisfaction Scale (GUESS) questionnaire.ResultsWe found significant positive associations for children’s (n=60) posttest knowledge with enjoyment (r58=0.36, P=.005) and user experience satisfaction (r58=0.27, P=.04). The children’s posttest knowledge scores were also positively correlated with challenge (r58=0.38, P=.003), knowledge improvement (r58=0.38, P=.003), and goal clarity (r58=0.29, P=.02) EGameFlow subscales and with narrative (r58=0.35, P=.006), creative freedom (r58=0.26, P=.04), and visual esthetics (r58=0.32, P=.01) GUESS subscales. Regression analysis indicated that the EGameFlow (F7,52=2.74, P=.02, R2=0.27) and the GUESS (F8,51=2.20, P=.04, R2=0.26) ratings significantly predicted the children’s posttest knowledge scores. EGameFlow challenge (β=0.40, t52=2.17, P=.04) and knowledge improvement (β=0.29, t52=2.06, P=.04) subscales significantly contributed to predicting children’s learning. None of the GUESS subscales significantly contributed to predicting children’s learning.ConclusionsThe findings of this study suggest that both enjoyment and user experience satisfaction for children aged 8 to 10 years were positively correlated with their learning and that were significant predictors of it. Challenge, knowledge improvement, narrative, creative freedom, and visual esthetics subscales correlated positively with children’s learning. In addition, challenge and knowledge improvement contributed to predicting their learning. These results are relevant to consider during the design stages of serious games developed for young children’s learning purposes.

Using Deficit Function to Determine the Minimum Fleet Size of an Autonomous Modular Public Transit System

Emerging technologies, such as connected and autonomous vehicles, electric vehicles, and information and communication, are surrounding us at an ever-increasing pace, which, together with the concept of shared mobility, have great potential to transform existing public transit (PT) systems into far more user-oriented, system-optimal, smart, and sustainable new PT systems with increased service connectivity, synchronization, and better, more satisfactory user experiences. This work analyses such a new PT system comprised of autonomous modular PT (AMPT) vehicles. In this analysis, one of the most challenging tasks is to accurately estimate the minimum number of vehicle modules, that is, its minimum fleet size (MFS), required to perform a set of scheduled services. The solution of the MFS problem of a single-line AMPT system is based on a graphical method, adapted from the deficit function (DF) theory. The traditional DF model has been extended to accommodate the definitions of an AMPT system. Some numerical examples are provided to illustrate the mathematical formulations. The limitations of traditional continuum approximation models and the equivalence between the extended DF model and an integer programming model are also provided. The extended DF model was applied, as a case study, to a single line of an AMPT system, the dynamic autonomous road transit (DART) system in Singapore. The results show that the extended DF model is effective in solving the MFS problem and has the potential to be applied to solving real-life MFS problems of large-scale, multi-line and multi-terminal AMPT systems.

VocalLock

Recent years have witnessed the surge of biometric-based user authentication for mobile devices due to its promising security and convenience. As a natural and widely-existed behavior, human speaking has been exploited for user authentication. Existing voice-based user authentication explores the unique characteristics from either the voiceprint or mouth movements, which is vulnerable to replay attacks and mimic attacks. During speaking, the vocal tract, including the static shape and dynamic movements, also exhibits the individual uniqueness, and they are hardly eavesdropped and imitated by adversaries. Hence, our work aims to employ the individual uniqueness of vocal tract to realize user authentication on mobile devices. Moreover, most voice-based user authentications are passphrase-dependent, which significantly degrade the user experience. Thus, such user authentications are pressed to be implemented in a passphrase-independent manner while being able to resist various attacks. In this paper, we propose a user authentication system, VocalLock, which senses the whole vocal tract during speaking to identify different individuals in a passphrase-independent manner on smartphones leveraging acoustic signals. VocalLock first utilizes FMCW on acoustic signals to characterize both the static shape and dynamic movements of the vocal tract during speaking, and then constructs a passphrase-independent user authentication model based on the unique characteristics of vocal tract through GMM-UBM. The proposed VocalLock can resist various spoofing attacks, while achieving a satisfactory user experience. Extensive experiments in real environments demonstrate VocalLock can accurately authenticate user identity in a passphrase-independent manner and successfully resist various attacks.

A Cloud-Edge Collaboration Framework for Cognitive Service

Mobile applications can leverage high-quality deep learning models such as convolutional neural networks and deep neural networks to provide high-performance cognitive services. Prior work on deep learning models-based mobile applications in a cloud-edge computing environment focuses on performing lightweight data pre-processing tasks on edge servers for cloud-hosted cognitive servers. These approaches have two major limitations. First, it is uneasy for the mobile applications to assure satisfactory user experience in terms of network communication delay, because the intermediary edge servers are used only to pre-process data (e.g., images and videos) and the cloud servers are used to complete the tasks. Second, these approaches assume the pre-trained deep learning models deployed on cloud servers are static, and will not attempt to automatically upgrade in a context-aware manner. In this article, we propose a cloud-edge collaboration framework that facilitates delivering cognitive services with long-lasting, fast response, and high accuracy properties. We fist deploy a shallow model (i.e., EdgeCNN) on the edge server and a deep model (i.e., CloudCNN) on the cloud server. EdgeCNN can provide durable and rapid response cognitive services, because edge servers not only provide computing resources for mobile applications, but also close to users. Then, we enable CloudCNN to assist in training EdgeCNN to improve the performance of the latter. Thus, EdgeCNN also provides high-accuracy cognitive services. Furthermore, because users may continue to upload data to edge servers in real-world scenarios, we propose to use the ongoing assistance of CloudCNN to further improve the accuracy of the shallow model. Experimental results show that EdgeCNN can reduce the average response time of cognitive services by up to 55.08 percent and improve accuracy by up to 26.70 percent.

Continuous Authentication Through Finger Gesture Interaction for Smart Homes Using WiFi

The development of smart homes has advanced the concept of user authentication to not only protecting user privacy but also facilitating personalized services to users. Along this direction, we propose to integrate user authentication with human-computer interactions between users and smart household appliances through widely-deployed WiFi infrastructures, which is non-intrusive and device-free. In this paper, we propose <inline-formula><tex-math notation="LaTeX">$FingerPass$</tex-math></inline-formula> which leverages channel state information (CSI) of surrounding WiFi signals to continuously authenticate users through finger gestures in smart homes. <inline-formula><tex-math notation="LaTeX">$FingerPass$</tex-math></inline-formula> separates the user authentication process into two stages, login and interaction, to achieve high authentication accuracy and low response latency simultaneously. In the login stage, we develop a deep learning-based approach to extract behavioral characteristics of finger gestures for highly accurate user identification. For the interaction stage, to provide continuous authentication in real time for satisfactory user experience, we design a verification mechanism with lightweight classifiers to continuously authenticate the user’s identity during each interaction of finger gestures. Experiments in real environments show that <inline-formula><tex-math notation="LaTeX">$FingerPass$</tex-math></inline-formula> can achieve the authentication accuracies of 90.6 percent under in-domain scenarios and 87.6 percent under cross-domain scenarios, as well as <inline-formula><tex-math notation="LaTeX">$186.6\;ms$</tex-math></inline-formula> response time during interactions.

Development and Pilot Test of a Virtual Reality Respiratory Biofeedback Approach

Breathing exercises with biofeedback have benefits over breathing exercises without biofeedback. However, the traditional measurement of respiratory signals that is required as part of feeding back the breath incurs high cost and effort. We propose a novel virtual reality (VR) based approach to respiratory biofeedback that utilizes the positionally tracked hand controllers integrated into modern VR systems to capture and feedback the respiration-induced abdominal movements. In a randomized controlled laboratory study, we investigated the feasibility and efficacy of the developed biofeedback algorithm. In total, 72 participants performed a short breathing exercise in VR with or without respiratory biofeedback. The feedback integration resulted in a satisfactory user experience, a heightened breath awareness, a greater focus on slow diaphragmatic breathing and an increased respiratory sinus arrhythmia. This evidences that the novel biofeedback approach is low-cost, unobtrusive, usable and effective in increasing breath awareness and promoting slow diaphragmatic breathing in the context of VR-based breathing exercises. Future studies need to investigate the broader applicability and long-term effects.

Incorporating Multi-Source Urban Data for Personalized and Context-Aware Multi-Modal Transportation Recommendation

Transportation recommendation is one important map service in navigation applications. Previous transportation recommendation solutions fail to deliver satisfactory user experience because their recommendations only consider routes in one transportation mode (uni-modal, e.g., taxi, bus, cycle) and largely overlook situational context. In this work, we propose <inline-formula><tex-math notation="LaTeX">$\mathsf {Hydra}$</tex-math></inline-formula> , a multi-task deep learning based recommendation system that offers multi-modal transportation planning and is adaptive to various situational context (e.g., nearby point-of-interest (POI) distribution and weather). We leverage the availability of existing routing engines and big urban data, and design a novel two-level framework that integrates uni-modal and multi-modal (e.g., taxi-bus, bus-cycle) routes as well as heterogeneous urban data for intelligent multi-modal transportation recommendation. In addition to urban context features constructed from multi-source urban data, we learn the latent representations of users, origin-destination (OD) pairs and transportation modes based on user implicit feedbacks, which captures the collaborative transportation mode preferences of users and OD pairs. Moreover, we propose two models to recommend the proper route among various uni-modal and multi-modal transportation routes: (1) a light-weight gradient boosting decision tree (GBDT) based recommendation model; and (2) a multi-task wide and deep learning (MTWDL) based recommendation model. We also optimize the framework to support real-time, large-scale route query and recommendation. We deploy <inline-formula><tex-math notation="LaTeX">$\mathsf {Hydra}$</tex-math></inline-formula> on Baidu Maps, <xref ref-type="fn" rid="fn1">¹</xref> <fn id="fn1"><label>1.</label> <uri>https://maps.baidu.com/</uri>. </fn> one of the world's largest map services. Real-world urban-scale experiments demonstrate the effectiveness and efficiency of our proposed system. Since its deployment in August 2018, <inline-formula><tex-math notation="LaTeX">$\mathsf {Hydra}$</tex-math></inline-formula> has answered over a hundred million route recommendation queries made by over ten million distinct users. The GBDT based model and MTWDL based model achieve 82.8 and 96.6 percent relative improvement of user click ratio, respectively.