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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CITATION STYLE
Yu, Z., Bu, J., Shen, C., Wang, W., Dai, L., Zhou, Q., & Zhao, C. (2020). A multi-site collaborative sampling for web accessibility evaluation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12376 LNCS, pp. 329–335). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58796-3_39
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