Abstract
Help centers are mainly designed to assist users with their product uses. The question as to how we measure the quality of a help center remains unanswered. As the frst step of a joint research initiated by Peking University and Baidu Cloud that aims to develop a set of computable metrics to evaluate the quality of help centers, this experience report shares the results of data analysis on correlation between user behavioral data and technical documentation quality. The documents and data we use are a suite of cloud computing services provided by Baidu Cloud. The report begins with an introduction of the research goal; following reviews on the related work, it then lays out the design of the experiments with user data collected from Baidu Cloud. In our experiments, we categorize all documents into three groups and try to identify which metrics would afect documentation quality most. The result shows that the key index that contributes most to the model is PV/UV. At last, the report concludes with our current experimental eforts and future work in our plan.
Original language | English |
---|---|
Title of host publication | SIGDOC 2019 |
Subtitle of host publication | Proceedings of the 37th ACM International Conference on the Design of Communication |
Place of Publication | New York, NY, USA |
Publisher | ACM Press |
Number of pages | 7 |
ISBN (Electronic) | 9781450367905 |
ISBN (Print) | 978-1-4503-6790-5 |
DOIs | |
Publication status | Published - 2019 |
Event | 37th ACM International Conference on the Design of Communication, SIGDOC 2019: Broadening the Boundaries of Communication Design - Portland State University, Portland, United States Duration: 4 Oct 2019 → 6 Oct 2019 Conference number: 37 |
Publication series
Name | SIGDOC 2019 - Proceedings of the 37th ACM International Conference on the Design of Communication |
---|
Conference
Conference | 37th ACM International Conference on the Design of Communication, SIGDOC 2019 |
---|---|
Abbreviated title | SIGDOC 2019 |
Country/Territory | United States |
City | Portland |
Period | 4/10/19 → 6/10/19 |
Keywords
- Help center evaluation
- Quality evaluation
- Technical information
- Web metrics