This study focuses on user experience from the perspective of big data to complete the aggregation, clustering, and visual presentation of knowledge. Using a combination of sample literature review, visualization technologies, knowledge map analysis, Carrot2 clustering, and other methodologies, this study intends to examine user experience from three perspectives: research state, hotspots, and trends. First, based on the double-map overlay, core institutions, core countries, core authors, core journals, and core references distribution research, the knowledge flow, research power, and research subjects of user experience are analyzed. Secondly, through keyword clustering analysis, this research intuitively presents the research topics of user experience and reveals the research hotspots and the evolution path of research methods. Finally, with the help of the subject clustering algorithm, the emerging trends of user experience research are predicted: the immersive experience upgrade of multi-scenario integration, the innovative design of multi-role collaboration, and the cross-disciplinary interactive exploration of multi-discipline. Following this, the user experience knowledge map is constructed, providing a global view and macro-cognition for subsequent research.
Targeting the problem of autonomous navigation of indoor robots in large-scale, complicated, and unknown environments, an autonomous online decision-making algorithm based on deep reinforcement learning is put forward in this paper. Traditional path planning methods rely on the environment modeling, which can cause more workload of calculating. In this paper, the sensors to detect surrounding obstacles are combined with the DDPG (deep deterministic policy gradient) algorithm to input environmental perception and control the action direct output, which enables robots to complete the tasks of autonomous navigation and distribution without relying on environment modeling. In addition, the algorithm preprocesses the relevant data in the learning sample with Gaussian noise, facilitating the agent to adapt to noisy training environment and improve its robustness. The simulation results show that the optimized DL-DDPG algorithm is more efficient on online decision-making for the indoor robot navigation system, which enables the robot to complete autonomous navigation and intelligent control independently.
To enhance the online education service experience, the emotional valence of the user was studied as an evaluation variable, and both qualitative and quantitative research were used to find how to evaluate online education service touchpoints. First, deconstruct the system service interface with the interactive touchpoint matrix, set service evaluation indicators from four aspects, visual guidance, learning resources, after-class evaluation, and interactive feedback, and build an online education service touchpoint evaluation system. Secondly, using Tencent Classroom as the target of research, an online education service rating experiment is created based on the two dimensions of emotional valence and perceptual cognition. With the aid of a questionnaire survey and analytic hierarchy process (AHP), a multidimensional evaluation of online education service touchpoints is accomplished using the learners’ emotional enjoyment, activation, dominance, touchpoint satisfaction, and importance as measuring indicators. Finally, concluding the assessment and optimization of online education service touchpoints, the evaluation data for the service are combined, and the evaluation results are generated using visual design. This study includes successful strategies and practical recommendations for boosting interest in e-learning services, user initiative, and excitement for learning.
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