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    Content-based recommendation systems can help users find relevant Tableau workbooks. This study develops a method using text analysis of workbook content to measure similarity, mimicking human judgment for better information retrieval.

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    Area of Science:

    • Information Science
    • Computer Science
    • Data Visualization

    Background:

    • Cloud-based visualization services increase accessibility to visual analytics.
    • Large repositories of interactive visualization workbooks form knowledge bases.
    • Difficulty in finding relevant information increases with repository size.

    Purpose of the Study:

    • Develop a content-based recommendation (CBR) system for visual analytic knowledge bases.
    • Enable analysts to find workbooks relevant to their interests.
    • Focus on text-based content representative of visualization subject matter.

    Main Methods:

    • Extract text-based content from Tableau workbook specifications.
    • Utilize natural language processing (NLP) techniques to compute workbook similarity.
    • Implement relevance measures using latent Dirichlet allocation (LDA) for a recommender tool.
    • Conduct a crowd-sourced user study to validate similarity measure against human judgment.

    Main Results:

    • Demonstrated extraction of relevant information from visualization specifications.
    • Showcased NLP techniques for computing similarity between workbooks.
    • User study indicated the developed similarity measure mimics human judgment.
    • A proof-of-concept recommender tool was built using LDA.

    Conclusions:

    • Text-based content analysis is effective for recommending visualization workbooks.
    • The developed similarity measure shows promise in improving information retrieval in large visual analytic knowledge bases.
    • Future work can refine NLP techniques and expand recommender system functionalities.