Abstract
This paper demonstrates a meta-search engine developed by the authors, which ranks the results based on web page information quality evaluation algorithm. The web page information quality score is calculated based on the title of the web page, the abstract of the web page and the source of the web page. The quality of web page can be evaluated by these factors. When a user submits an input, the proposed meta-search engine system collects the results from some general search engines like Baidu, Bing, Sogou and so on, and rank the web pages according to their information quality scores. Because we do not need a local database to store a large amount of data, all operations are completed in the cache, which greatly reduces system consumption. The system is evaluated by three kinds of representative queries, and the results show that its search accuracy and user experience are obviously better than the current general search engines.
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References
CNNIC Internet research. The 43rd CNNIC China Internet Report Released. China Broadcasting, 4 (2019)
Sahoo, P., Parthasarthy, R.: An efficient web search engine for noisy free information retrieval. Int. Arab J. Inf. Technol. 15(3), 412–418 (2018)
FuYong, Y., JinDong, W.: An implemented rank merging algorithm for meta search engine. In: International Conference on Research Challenges in Computer Science, pp. 191–193. IEEE Computer Society (2009)
Kumar, J., Kumar, R., Dixit, M.: Result merging in meta-search engine using genetic algorithm. In: International Conference on Computing, Communication and Automation, ICCCA 2015, pp. 299–303. IEEE (2015)
Järvelin, K., Kekäläinen, J.: IR evaluation methods for retrieving highly relevant documents. In: SIGIR, pp. 41–48 (2000)
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Li, Y., Ye, Y., Xu, W. (2020). A Meta-Search Engine Ranking Based on Webpage Information Quality Evaluation. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12318. Springer, Cham. https://doi.org/10.1007/978-3-030-60290-1_46
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DOI: https://doi.org/10.1007/978-3-030-60290-1_46
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