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We discuss the challenges associated with creating a content-based recommendation system based on visualization specifications and explore more concretely how to implement the relevance measures required using Tableau workbook specifications as the source of content data. We also demonstrate what information can be extracted from these visualization specifications and how various natural language processing techniques can be used to compute similarity between workbooks as one way to measure relevance. We report on a crowd-sourced user study to determine if our similarity measure mimics human judgement. Finally, we choose latent Dirichlet allocation (LDA) as a specific model and instantiate it in a proof-of-concept recommender tool to demonstrate the basic function of our similarity measure.