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Leveraging Community-Generated Videos and Command Logs to Classify and Recommend Software Workflows

Published: 21 April 2018 Publication History

Abstract

Users of complex software applications often rely on inefficient or suboptimal workflows because they are not aware that better methods exist. In this paper, we develop and validate a hierarchical approach combining topic modeling and frequent pattern mining to classify the workflows offered by an application, based on a corpus of community-generated videos and command logs. We then propose and evaluate a design space of four different workflow recommender algorithms, which can be used to recommend new workflows and their associated videos to software users. An expert validation of the task classification approach found that 82% of the time, experts agreed with the classifications. We also evaluate our workflow recommender algorithms, demonstrating their potential and suggesting avenues for future work.

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  1. Leveraging Community-Generated Videos and Command Logs to Classify and Recommend Software Workflows

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    cover image ACM Conferences
    CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
    April 2018
    8489 pages
    ISBN:9781450356206
    DOI:10.1145/3173574
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    Published: 21 April 2018

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    Author Tags

    1. application logs
    2. community-generated videos
    3. software learning
    4. topic modeling
    5. workflow recommendation

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    CHI '18 Paper Acceptance Rate 666 of 2,590 submissions, 26%;
    Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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    View all
    • (2024)Tutorial mismatches: investigating the frictions due to interface differences when following software video tutorialsProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661511(1942-1955)Online publication date: 1-Jul-2024
    • (2024)The Impact of Sketch-guided vs. Prompt-guided 3D Generative AIs on the Design Exploration ProcessProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642218(1-18)Online publication date: 11-May-2024
    • (2023)Beyond Instructions: A Taxonomy of Information Types in How-to VideosProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581126(1-21)Online publication date: 19-Apr-2023
    • (2023)Identifying Multimodal Context Awareness Requirements for Supporting User Interaction with Procedural VideosProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581006(1-17)Online publication date: 19-Apr-2023
    • (2022)PONI: A Personalized Onboarding Interface for Getting Inspiration and Learning About AR/VR CreationNordic Human-Computer Interaction Conference10.1145/3546155.3546642(1-14)Online publication date: 8-Oct-2022
    • (2022)SoftVideo: Improving the Learning Experience of Software Tutorial Videos with Collective Interaction DataProceedings of the 27th International Conference on Intelligent User Interfaces10.1145/3490099.3511106(646-660)Online publication date: 22-Mar-2022
    • (2022)SimCURL: Simple Contrastive User Representation Learning from Command Sequences2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA55696.2022.00186(1143-1150)Online publication date: Dec-2022
    • (2021)HelpViz: Automatic Generation of Contextual Visual Mobile Tutorials from Text-Based InstructionsThe 34th Annual ACM Symposium on User Interface Software and Technology10.1145/3472749.3474812(1144-1153)Online publication date: 10-Oct-2021
    • (2021)RubySlippers: Supporting Content-based Voice Navigation for How-to VideosProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445131(1-14)Online publication date: 6-May-2021
    • (2020)Goal-driven Command Recommendations for AnalystsProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3412255(160-169)Online publication date: 22-Sep-2020
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