Data sparsity in pervasive computing and human–machine interaction: a survey of sources, structures, and remedies

Data sparsity is a central challenge in pervasive computing and human–machine interactive systems, where observations arise unevenly across users, devices, algorithms, and environments. Across recommender interfaces, healthcare monitoring, smart-city mobility, and language-enabled interaction, sparsity can reflect incomplete sensing, constrained exposure, intermittent participation, privacy limits, and heterogeneous event generation. These mechanisms can produce missing or weakly supported instances as well as observable structures such as excess zeros and long-tail frequency distributions; conflating mechanisms with their manifestations can lead to mismatched modelling and evaluation. This survey synthesises 134 studies and develops a source-to-structure diagnostic framework. It distinguishes two mechanism-oriented perspectives—data collection oriented sparsity and user–system interaction oriented sparsity—from a distributional structure oriented perspective that records observable zero-inflated and long-tail patterns without assuming their causes. Six operational forms are connected through a support-based chain from mechanism to observational support, observable structure, and intervention. The roadmap further distinguishes evidence acquisition, bias correction, robust learning under fixed sparsity, and decentralised utilisation, and specifies cross-domain transfer conditions, mechanism- and structure-aware evaluation, and human-centred trade-offs for conditional method selection.