Abstract
Many sampling methods have been used for web accessibility evaluation. However, due to the difficulty of web page feature extraction and the lack of unsupervised clustering algorithm, the result is not very good. How to optimize the manual workload of different websites under the premise of ensuring that the overall manual workload remains the same during multi-site collaborative sampling is an important issue at present. To resolve the above problems, we propose a multi-site collaborative sampling method to obtain the final sampling result of each website. The effectiveness of the two sampling methods proposed in this paper is proved by experiments on real website datasets.
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Acknowledgments
This work is supported by Alibaba-Zhejiang University Joint Institute of Frontier Technologies, The National Key R&D Program of China (No. 2018YFC2002603, 2018YFB1403202), Zhejiang Provincial Natural Science Foundation of China (No. LZ13F020001), the National Natural Science Foundation of China (No. 61972349, 61173185, 61173186) and the National Key Technology R&D Program of China (No. 2012BAI34B01, 2014BAK15B02).
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Yu, Z. et al. (2020). A Multi-site Collaborative Sampling for Web Accessibility Evaluation. In: Miesenberger, K., Manduchi, R., Covarrubias Rodriguez, M., Peňáz, P. (eds) Computers Helping People with Special Needs. ICCHP 2020. Lecture Notes in Computer Science(), vol 12376. Springer, Cham. https://doi.org/10.1007/978-3-030-58796-3_39
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DOI: https://doi.org/10.1007/978-3-030-58796-3_39
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