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The artistic design of user interaction experience for mobile systems based on context-awareness and machine learning

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Abstract

This paper investigates the art design of user interaction experience in mobile systems through the methods of contextual perception and machine learning. The theoretical foundations for the design of intangible cultural heritage interactive display resources include digital display theory, intangible cultural heritage education theory, embodied cognition theory, and gamification design theory. Based on the modelling and analysis of the theoretical foundations, the design principles are derived, including the heritage education principle, the somatic interaction design principle, and the content design principle. In this paper, we use ontologies to construct user knowledge models, fuse multi-situational similarity metrics, screen out candidate neighbour sets through preliminary screening, then combine the user's activity to construct time-based weight tensor scores, use tensor decomposition to obtain recommendation evaluation values, and finally use the recommendation evaluation values to make artistic recommendations. The experimental results show that the algorithm can still obtain a good recommendation implementation in the case of extremely sparse data. The analysis of the fit between augmented reality and folk art appreciation class, as well as the reference to relevant application cases, make design and practice of augmented reality application in folk art appreciation class, try to solve the common problems in folk art appreciation class, analyse the feedback effect and make a summary and outlook.

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Acknowledgements

This work was supported by the Scientific Research Fund in the Field of Humanities and Social Sciences of Henan University of Science and Technology (No: 2014SQN014).

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  1. School of Art and Design, Henan University of Science and Technology, Luoyang, 471000, China

    Lina Liu

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  1. Lina Liu
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Correspondence to Lina Liu.

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Liu, L. The artistic design of user interaction experience for mobile systems based on context-awareness and machine learning. Neural Comput & Applic 34, 6721–6731 (2022). https://doi.org/10.1007/s00521-021-06160-x

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