Single User Research Articles

Group travel recommendation model study based on spatial and temporal integration factors

With the increasing demand for tourism, people’s travel modes are more and more diversified, and the tourism recommendation system also arises at the historical juncture. However, the current recommendation system is only recommended for a single user and does not realize the group travel recommendation. To achieve the goal of recommending its preferred attractions for multiple users, the time decay characteristics and Pearson correlation coefficient in Newton’s cooling law are used to obtain the user similarity with spatial distance factor and temporal decay factor and to obtain the score prediction results based on spatiotemporal fusion. In addition, the trust of user communication is used to recommend, and the weights of the two scoring results are added to obtain the personalized recommendation results of member users. Finally, the study used the fusion strategy to integrate the personalized recommendation results for group preference and obtained the final group travel recommendation list. Therefore, a group travel recommendation model based on spatio-temporal integration factors was constructed. According to the experimental analysis, we can see that the average HR value of the constructed model is 0.8124, and the average NDCG value is 0.7284, which can accurately judge users’ preferences and get the most suitable group travel recommendation results, thus facilitating users to make the next plan for the tourism project.

The Effects of Human-Robot Interactions and the Human-Robot Relationship on Robot Competence, Trust, and Acceptance

As social robots may be used by a single user or multiple users different social scenarios are becoming more important for defining human-robot relationships. Therefore, this study explored human-robot relationships between robots and users in different interaction modes to improve user interaction experience. Specifically, education and companion were selected as the most common areas in the use of social robots. The interaction modes used include single-user interaction and multi-user interaction. The three human-robot relationships were adopted. The robot competence scale, human-robot trust scale, and acceptance of robot scale were used to evaluate subjects’ views on robots. The results demonstrate that in the two scenarios, people were more inclined to maintain a more familiar and closer relationship with the social robot when the robot interacted with a single user. When multiple persons interact in an education scenario, setting the robot to Acquaintance relationships is recommended to improve its competence and people’s trust in the robot. Similarly, in multi-person interaction, Acquaintance relationships would be more accepted and trusted by people in a companion scenario. Based on these results, robot sensors can be added to further optimize human-robot interaction sensing systems. By identifying the number of users in the interaction environment, robots can automatically employ the best human-robot relationship for interaction. Optimizing human-robot interaction sensing systems can also improve robot performance perceived in the interaction to meet different users’ needs and achieve more natural human-robot interaction experiences.

Fairness‐aware eMBB/URLLC spectrum resource multiplexing to ensure reliability in vehicular networks

AbstractSpectrum resource multiplexing of enhanced Mobile BroadBand (eMBB) and Ultra‐Reliable and Low Latency Communications (URLLC) via puncturing in vehicular networks has become an important research direction. The main issue in this direction is how to minimize the adverse impact of preemptive puncturing on eMBB traffic. However, the available eMBB/URLLC coexisting schemes lack considerations on the quality of service of a single eMBB user and fairness between users. In view of this, we formulate the eMBB/URLLC multiplexing problem to maximize eMBB reliability and fairness under the premise of meeting the requirements of URLLC as a nonlinear integer programming (NIP) in this article. To balance the performance and computational complexity, we decompose this NIP into two subproblems that be solved efficiently. For the eMBB spectrum resource allocation subproblem of maximizing the reliability and fairness under resource constraints, we prove that this problem is NP‐hard, and design a Deep Q‐Network (DQN)‐based spectrum resource allocation algorithm and a two‐stage heuristic spectrum resource allocation algorithm, respectively. For the URLLC preemption subproblem, we further propose bandwidth‐sensitive URLLC spectrum resource preemption algorithm and time‐sensitive URLLC spectrum resource preemption algorithm, respectively. Simulation results show that the proposed scheme can guarantee better fairness and reliability for eMBB users compared with the comparison schemes. Besides, for the eMBB resource allocation problem, the proposed scheme can achieve near‐optimal performance with low complexity.

Cybercrime Classification: A Victimology-based approach

The need for understanding cybercrime and the possibility of its occurrence is significant to mitigate its adverse effects on society. A comprehensive universally agreed-upon classification scheme for cybercrime is hugely lacking in terms of utilizing a complete perspective of the entities involved in the same. A new perspective in cybercrime classification is moving beyond the machines and focusing on the humans, especially the victims. Cyber victimization extends from the single user to a mass or system perspective, thereby representing governments, organizations, and society to be categorized as victims. This paper proposes a novel ontological classification of cyber victimology that can help illustrate the complete cybercrime incident from the perspective of the victim. We utilize a multidimensional typology to represent the dimensions and classifications of the cybercrime victim. We then analyse the semantic relationships between the ontological objects to develop a comprehensive victimology representation. The understanding of the type and role of the victim provides new insight into the analysis of the cyber incident. Moreover, the resultant representation can serve as an extension to current cybercrime ontological frameworks and help in providing a new point of defence in cybercrime incidents. Finally, such a victimology-based classification can subsequently result in a dynamic ontology which can be queried to obtain relevant insights into the nature and occurrence of cybercrimes.

Simulation modeling of the system of managing the psychophysiological state of a person based on non-verbal extralinguistic characteristics

Today, when artificial intelligence systems are being created, the time of constant dia-logue between humans and computer, there is a great demand for intelligent control systems and human-machine systems. These systems are very complex and require analysis and re-search. The publication is devoted to the simulation computer modeling of control of the psy-chological and physical state of a person. The basis of the human-computer dialogue system is the speech-to-text conversion sys-tem. The use of this system is the basis for the creation of computer language control systems. Unlike systems that work on the principle of identifying a single user response to a request and a proposal or a question from a password database, it is necessary to expand the lan-guage interface of the computer. It is necessary for the computer to accept commands from the operator and execute them only if the voice and its timbre match those registered in the database. Such a system will allow you to differentiate access to the computer and ensure se-curity for the user, preserve his privacy or save his personal data. After analyzing the speech activity of the operator, it became clear that it was necessary to develop a system of command control and «speech-to-text» conversion in the fused speech stream. This task is solved by comparing the words of the commands and is part of the problem of automatic recognition and understanding of the operator's human language. On the basis of such systems, a computer language control system can be created. The analysis has shown that unlike text-dependent identification systems, systems with a dialog method implement not only a one-time operator response to a query or question from the password database, but also the extension of its full-fledged language interface. The computer receives commands from the operator and executes them only if the voice matches the one registered in the database. This analysis is effective in the context of real-life speech activity of the operator. The literature review showed that for the development of sys-tems of command control and "speech-to-text" transformation in a stream of fused speech, it is necessary to use "mechanisms" of automatic recognition and understanding of the opera-tor's language.

CRUD Application Using ReactJS Hooks

This Project is aimed to implement CRUD operations using the library called React.js which is the advanced version of the language Javascript. The question which pops out everyone’s mind now is why React? There’s lot of open – source framework/ library/ language that is available in today’s internet. The purpose of using react is anyone with the little knowledge of javascript can easily learn react and implementation of this library is user-friendly. Also, there’s many more packages react can easily get installed in NPM and run the application whatever changes in the code can do. React also delivers good UI for the developer and to create/develop new apps for future generation. There are more than 2000 developers and over one lakh sites making use of react.js among some of the most powerful are Instagram, Netflix, Whatsapp, Uber. It also integrates with any other javascript libraries or framework and speciality of react can make changes in the web browser without doing subsequent refresh of the pages. React can allow the developer to build any complex web application and more importantly mobile application. The CRUD is an acronym for Create, Read, Update and Delete which is very much essential for implementing any strong application with relational Database. Thus, the project results in Creating the user/users with their details, read the users task, updating if any new user is created through ‘Create’ operation, and finally delete the user/users list if not wanted for an application. Though the ‘crud’ functions for creating an application can be done in any language, the author specifically uses Javascript library called react.js which is one of the best platforms to create the single page user application interfaces. The overarching goal of this application is to bring easiness for any product development company in need of crud-user functions can stick to this.

Optimizing echo state networks for continuous gesture recognition in mobile devices: A comparative study

Continuous gesture recognition can be used to enhance human-computer interaction. This can be accomplished by capturing human movement with the use of the Inertial Measurement Units in smartphones and using machine learning algorithms to predict the intended gestures. Echo State Networks (ESNs) consist of a fixed internal reservoir that is able to generate rich and diverse nonlinear dynamics in response to input signals that capture temporal dependencies within the signal. This makes ESNs well-suited for time series prediction tasks, such as continuous gesture recognition. However, their application has not been rigorously explored, with regard to gesture recognition. In this study, we sought to enhance the efficacy of ESN models in continuous gesture recognition by exploring diverse model structures, fine-tuning hyperparameters, and experimenting with various training approaches. We used three different training schemes that used the Leave-one-out Cross-validation (LOOCV) protocol to investigate the performance in real-world scenarios with different levels of data availability: Leaving out data from one user to use for testing (F1-score: 0.89), leaving out a fraction of data from all users to use in testing (F1-score: 0.96), and training and testing using LOOCV on a single user (F1-score: 0.99). The obtained results outperformed the Long Short-Term Memory (LSTM) performance from past research (F1-score: 0.87) while maintaining a low training time of approximately 13 seconds compared to 63 seconds for the LSTM model. Additionally, we further explored the performance of the ESN models through behaviour space analysis using memory capacity, Kernel Rank, and Generalization Rank. Our results demonstrate that ESNs can be optimized to achieve high performance on gesture recognition in mobile devices on multiple levels of data availability. These findings highlight the practical ability of ESNs to enhance human-computer interaction.

A study on the ambient electromagnetic radiation level of 5G base stations in typical scenarios

Knowledge of the electromagnetic radiation characteristics of 5G base stations under different circumstances is useful for risk prevention, assessment, and management. This paper selects several typical scenes (Open spaces, building concentration areas, user and building intensive areas) for electromagnetic radiation monitoring, and analyzes the relationship between ambient radiated power density and base station background. The results show that the factors that have significant impacts on the environmental radiation power density of 5G base stations including transmission distance, base station distribution, user density, building reflection superposition and so on. The radiation energy decays rapidly with distance. When the density of the building distribution is too large, the superposition effect caused by the reflected wave is concentrated at the distance of 50-70 meters. When the user density decreases (the superposition effect of reflected waves decreases), the 5G monitoring value follows the direct wave attenuation law and decreases rapidly with the increase of distance. Points with higher measured radiation in the simple access condition also had higher measured radiation in the high-speed download condition. With the popularization of 5G mobile phones and the increase of user density, the resource utilization of a single user will decline to the normal operation state, and the radiation environmental impact will be further reduced.

Orthogonal beamforming technique for massive MIMO systems

AbstractBeamforming represents a pivotal technology in massive multiple-input multiple-output (MIMO) systems, as it facilitates the regulation of transmission and reception operations. Beamforming techniques’ categorization is based either on their hardware architecture or implementation strategy. This paper proposes an orthogonal beamforming technology founded on a specific implementation method that utilizes predetermined orthogonal beams to serve users. The suggested approach incorporates numerous orthogonal beams relying on a substantial number of antennas at the base station. The primary objective of this approach is to enhance the performance of massive MIMO systems by augmenting spectral efficiency and accommodating more users. The proposed beamforming approach is well suited for millimeter frequency bands. The purpose of this paper is to explore the suggested orthogonal beamforming technology. The concept of this approach is described at first and then followed by an evaluation of its efficacy for a single user through the allocation of orthogonal beams. The suggested approach is also examined in the context of multiuser systems, and the results are compared with the adaptive ZF beamforming technique. Furthermore, the paper presents solutions to the issues that may arise in multiuser systems, for example, ensuring that each orthogonal beam is assigned to only one user. The simulations conducted in this study demonstrate that the suggested approach outperforms the ZF technique in terms of both the spectral efficiency and the number of serviced users. Specifically, the suggested approach can enhance SE by approximately 40.6% over the ZF technique, and it can support up to double the number of users when compared to the ZF approach.

Intelligent Reflecting Surface Framework for ED Based Spectrum Sensing

According to a recent study, adding intelligent reflecting surfaces (IRS) to propagation environments improves the performance of wireless devices. The capabilities of cognitive radio’s spectrum sensing over environments with assisted wireless propagation via IRS are examined in this research. Taking channel fading effects into consideration, we derive closed-form analytical expressions for the average probability of detection (APD) for single user and cooperative users. Further, the derived APD expression is used to derive the average area under the receiver operating characteristics curve (AUC¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\overline{AUC}}$$\\end{document}) expression. Monte Carlo simulations are also carried out in order to validate the derived expressions. Results demonstarte the extent of detectability performance in cognitive radio on utilizing the IRS aided wireless propagation environment therefore witnessing its significance for upcoming wireless technological advancement by offering spectrum efficiency with good quality of service (QoS).

The More-Than-Human Trend in Design Research: A Literature Review

The implications of contemporary technological and environmental changes are driving a transition in human practices toward approaches that widen and shift the focus beyond human needs. These approaches leverage new ideas and concepts coming from the posthumanist perspective, which has been gaining momentum across several disciplines, including the design field. As several researchers have started to take interest in those themes, experimental methods and practices have been growing along with different definitions, which may accentuate the complexity of producing consistent advances in the discipline. The objective of the article is to review the existing literature on design practices and approaches that, during the last decade, have evolved beyond the focus of a single user and are thus defined with terms such as More-Than-Human Centered Design, Ecosystemic Design, Posthuman Design, etc. The outputs of the integrative literature review offer a clearer picture of the phenomenon.

Sustainable Data-Driven Strategies and Active Well-Being: A Case Study

Nowadays the world is characterized by an increasingly aging society. This phenomenon represents a risk for the sustainability of the healthcare system. One of the factors that can accelerate the consequences of the aging process is sedentary behavior. Currently, there is a large availability of wearable smart devices and mobile applications, that can collect data. However, some social and functional problems were observed, therefore we believe that there is great room for improvement (Mincolelli et al., 2018). In addition, the paradigm of the Internet of Things is enabling those applications to share data with other programs and smart devices, which, in turn, can generate an ecosystem of services that can improve the quality of life, not only of the single user, but of many groups of a determined social context. The contribution presents a case study (PLEINAIR project) that focuses specifically on the development of Human-Centered outdoor smart technologies that can adapt themselves to the necessities of people of all ages and abilities in order to encourage them of taking care of their health.

Towards cross-silo federated learning for corporate organizations

Digital media companies rely on machine learning models to target their content toward their audience’s interests. Machine learning models usually rely on the amount and quality of training data. While today, data is abundant, it is typically stored in data silos and cannot be shared between companies or publishers due to data protection and user privacy. Federated Learning (FL) is a distributed machine learning approach that is rapidly gaining popularity and enables collaboratively training machine learning models on a large corpus of decentralized data. Prior research on FL mainly focuses on an FL setup containing millions of clients. For example, a client may be a single user’s mobile device with data. However, we note that, in many scenarios, corporate organizations such as news media companies that have available data from multiple sets of users could also benefit from FL. In this work, we aim to focus on building FL models where multiple corporate organizations like news media companies or banks participate in the training process of FL to collaboratively train federated models. We used federated learning to train models for a set of corporate stakeholders and applied FL for two tasks: a classification task and a ranking task. For the classification task, we designed a tree-based federated random forest algorithm and a neural network-based federated algorithm. For the ranking task, we designed a federated neural ranking model for news article recommendations. Our experimental results demonstrate that corporate companies by participating in FL can achieve benefits in improving the model performance in terms of accuracy for classification tasks and in terms of ranking for recommendation tasks. Furthermore, we designed and developed a simple framework for a small number of stakeholders to train federated models.

Rapid Design of an ICU Ventilator: An Approach Based on Smart Switching of Compressed Air-Oxygen.

Breathing support is provided by regulating volume or pressure of lungs using ventilators. Due to COVID-19 pandemic, there was a sudden shortage of resuscitating devices such as ventilators. Additionally, ventilators being one of the primary critical care devices are also very costly. In order to address this situation, a number of low-cost designs have been proposed, however, many of these lack either an efficient control system or a hardware comparable to a standard ICU ventilator. In this regard, this article presents a comprehensive cost-effective solution that covers all aspects of the ventilator design (named as NED-Vent) such as hardware/pneumatic assembly, electronic design, user interface and control system. The proposed design works on compressed air-oxygen switching via proportional valves to produce basic volume and pressure modes as well as their derivatives such as assist ventilation, intermittent ventilation and spontaneous modes. The NED-Vent also features an interactive single-knob-single-touch user interface along with an automated mechanism for adjusting air-oxygen ratio in breathing gas mixture as an improvement on existing designs. The pressure regulated control is based on two mathematical models of human lungs with dynamic lung parameters estimated using machine learning approach. Additionally, the controller is tuned to optimized stability using Jury's Test and Ziegler-Nichols methods. The obtained results of breath profile are validated for the employed mathematical models (SLSC & SLMC) as well as the tuning methods in compliance with the tolerances provided by international standards.

On the Application of Quasi-Degradation to Network NOMA in Downlink CoMP Systems

The application of network non-orthogonal multiple access (N-NOMA) technique to coordinated multi-point (CoMP) systems has attracted significant attention due to its superior capability to improve connectivity and maintain reliable transmission for CoMP users simultaneously. Based on the concept of quasi-degraded channel for N-NOMA, this paper studies the precoding design for downlink N-NOMA scenarios with two base stations (BSs) equipped with multiple antennas. In specific, under quasi-degraded channels, simple linear precoding based N-NOMA can achieve the same minimal total transmission power as theoretically optimal but complicated dirty paper coding (DPC) scheme, when the users’ target rates and minimal transmission power of each BS are given. In this paper, the channel quasi-degradation (QD) condition is first rigorously derived for the scenario with single CoMP user and two NOMA users. The closed-form optimal precoders for N-NOMA under quasi-degraded channels are also provided. Then, based on QD condition, a novel hybrid N-NOMA (H-N-NOMA) scheme is proposed, which is a mixture of N-NOMA and conventional zero-forcing beamforming (ZFBF) scheme. Further, for the scenarios with more users, a low-complexity QD based user pairing (QDUP) algorithm is proposed, which is then combined with H-N-NOMA to make a novel scheme termed H-N-NOMA/QDUP into being. Numerical results are presented to reveal the impact factors on QD channels, and also demonstrate the superior performance of the proposed H-N-NOMA/QDUP scheme. It is shown that the proposed H-N-NOMA/QDUP scheme can effectively exploit the benefit of multi user diversity.

A Multi-Agent System for Fine-Grained Opinion Dynamics Analysis in Online Social Networks

The influence and dissemination of users’ opinions are the essence of the opinion dynamics in online social networks (OSNs). Understanding the process of users’ opinion formation and propagation can provide better service for public opinion monitoring and product advertising. However, most opinion dynamics research typically do not distinguish between user opinion formation and propagation, instead focusing on the process of mass opinion polarization. This article presents a multiagent system (MAS) to analyze fine-grained opinion dynamics (FOD) by agent-based modeling (ABM). At the macro level, the MAS-FOD system we designed can produce a statistical analysis of public opinion evolution based on different parameter constellations and analyze the changes in personalized agent opinions. At the micro level, the agent-based model is presented to describe the single user in OSNs as an individualized agent in MAS-FOD. Specifically, we propose two mechanisms, agent-based opinion formation (AOF) mechanism and agent-based opinion propagation (AOP) mechanism, for agents to form and disseminate opinions, respectively. Meanwhile, multidimensional social influence features mining from self, local neighbors, and global topic community are defined and used in two mechanisms to train personalized agents to self-adapt to the MAS-FOD system. We demonstrate the rationality and effectiveness of the MAS-FOD system from two types of experiments: empirical analysis and simulation analysis. The former is driven by empirical social network data to analyze the predictive performance of AOF; the latter simulates the public opinion propagation through the AOP mechanism in the MAS-FOD system. The results demonstrate that: 1) the AOF outperforms SOTA methods in the accuracy of agent opinion prediction; 2) the dissemination effect based on the AOP is more closer to the actual evolution trend of public opinion than the baseline; and 3) the MAS-FOD system can perform fine-grained analysis of opinion dynamics by adjusting different parameters.

A personalized semi-automatic sleep spindle detection (PSASD) framework

BackgroundSleep spindles are distinct electroencephalogram (EEG) patterns of brain activity that have been posited to play a critical role in development, learning, and neurological disorders. Manual scoring for sleep spindles is labor-intensive and tedious but could supplement automated algorithms to resolve challenges posed with either approaches alone. New methodsA Personalized Semi-Automatic Sleep Spindle Detection (PSASD) framework was developed to combine the strength of automated detection algorithms and visual expertise of human scorers. The underlying model in the PSASD framework assumes a generative model for EEG sleep spindles as oscillatory components, optimized to EEG amplitude, with remaining signals distributed into transient and low-frequency components. ResultsA single graphical user interface (GUI) allows both manual scoring of sleep spindles (model training data) and verification of automatically detected spindles. A grid search approach allows optimization of parameters to balance tradeoffs between precision and recall measures. Comparison with existing methodsPSASD outperformed DETOKS in F1-score by 19% and 4% on the DREAMS and P-DROWS-E datasets, respectively. It also outperformed YASA in F1-score by 25% in the P-DROWS-E dataset. Further benchmarking analysis showed that PSASD outperformed four additional widely used sleep spindle detectors in F1-score in the P-DROWS-E dataset. Titration analysis revealed that four 30-second epochs are sufficient to fine-tune the model parameters of PSASD. Associations of frequency, duration, and amplitude of detected sleep spindles matched those previously reported with automated approaches. ConclusionsOverall, PSASD improves detection of sleep spindles in EEG data acquired from both younger healthy and older adult patient populations.

Methodology to Investigate Effect of Prosthetic Interface Design on Residual Limb Soft Tissue Deformation.

Residual limb discomfort and injury is a common experience for people living with lower limb amputation. Frequently, inadequate load distribution between the prosthetic device and the residual limb is the root cause of this issue. To advance our understanding of prosthetic interface fit, tools are needed to evaluate the mechanical interaction at the prosthetic interface, allowing interface designs to be evaluated and optimised. Present a methodology report designed to facilitate comprehension of the mechanical interaction between the prosthetic interface and the residual limb. As a pilot study, this methodology is used to compare a hands-on and hands-off interface for a single transtibial prosthesis user using secondary Magnetic Resonance Imaging (MRI) data. MRI data of the residual limb while wearing a prosthetic interface is segmented into a hard tissue and a skin surface model. These models are exported as stereolithography (STL) files. Two methods are used to analyse the interface designs. Firstly, CloudCompare software is used to compute the nearest vertex on the skin surface for every vertex on the compiled internal bony surface for both interface types. Secondly, CloudCompare software is used to compare registered skin surfaces of the residual limb while wearing the hands-on and hands-off interfaces. The maximum and minimum nearest distances between the internal bony surface and skin surface were similar between interface types. However, the distribution of nearest distances was different. When comparing the skin surface while wearing both interfaces, where the fit is more compressive can be visualized. For the dataset used in this study, the classic features of a hands-on Patella Tendon Bearing interface and hands-off pressure cast interface could be identified. The methodology presented in this report may give researchers a further tool to better understand how interface designs affect the soft tissues of the residual limb.

Remote Multi-Person Heart Rate Monitoring with Smart Speakers: Overcoming Separation Constraint.

Heart rate is a key vital sign that can be used to understand an individual's health condition. Recently, remote sensing techniques, especially acoustic-based sensing, have received increasing attention for their ability to non-invasively detect heart rate via commercial mobile devices such as smartphones and smart speakers. However, due to signal interference, existing methods have primarily focused on monitoring a single user and required a large separation between them when monitoring multiple people. These limitations hinder many common use cases such as couples sharing the same bed or two or more people located in close proximity. In this paper, we present an approach that can minimize interference and thereby enable simultaneous heart rate monitoring of multiple individuals in close proximity using a commonly available smart speaker prototype. Our user study, conducted under various real-life scenarios, demonstrates the system's accuracy in sensing two users' heart rates when they are seated next to each other with a median error of 0.66 beats per minute (bpm). Moreover, the system can successfully monitor up to four people in close proximity.

Practical and privacy-preserving geo-social-based POI recommendation

With the rapid development of location-based services and online social networks, POI recommendation services considering geographic and social factors have received extensive attention. Meanwhile, the vigorous development of cloud computing has prompted service providers to outsource data to the cloud to provide POI recommendation services. However, there is a degree of distrust of the cloud by service providers. To protect digital assets, service providers encrypt data before outsourcing it. However, encryption reduces data availability, making it more challenging to provide POI recommendation services in outsourcing scenarios. Some privacy-preserving schemes for geo-social-based POI recommendation have been presented, but they have some limitations in supporting group query, considering both geographic and social factors, and query accuracy, making these schemes impractical. To solve this issue, we propose two practical and privacy-preserving geo-social-based POI recommendation schemes for single user and group users, which are named GSPR-S and GSPR-G. Specifically, we first utilize the quad tree to organize geographic data and the MinHash method to index social data. Then, we apply BGV fully homomorphic encryption to design some private algorithms, including a private max/min operation algorithm, a private rectangular set operation algorithm, and a private rectangular overlapping detection algorithm. After that, we use these algorithms as building blocks in our schemes for efficiency improvement. According to security analysis, our schemes are proven to be secure against the honest-but-curious cloud servers, and experimental results show that our schemes have good performance.