Author: Yu, Zhi; Bu, Jiajun; Shen, Chao; Wang, Wei; Dai, Lianjun; Zhou, Qin; Zhao, Chuanwu
Title: A Multi-site Collaborative Sampling for Web Accessibility Evaluation Cord-id: d6pb1ktd Document date: 2020_8_10
ID: d6pb1ktd
Snippet: 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 sampl
Document: 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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