  In insight recommendation systems, obtaining timely and high-quality
recommended visual analytics over incomplete data is challenging due to the
difficulties in cleaning and processing such data. Failing to address data
incompleteness results in diminished recommendation quality, compelling users
to impute the incomplete data to a cleaned version through a costly imputation
strategy. This paper introduces VizPut scheme, an insight-aware selective
imputation technique capable of determining which missing values should be
imputed in incomplete data to optimize the effectiveness of recommended
visualizations within a specified imputation budget. The VizPut scheme
determines the optimal allocation of imputation operations with the objective
of achieving maximal effectiveness in recommended visual analytics. We evaluate
this approach using real-world datasets, and our experimental results
demonstrate that VizPut effectively maximizes the efficacy of recommended
visualizations within the user-defined imputation budget.
