Abstract
The information on the web is increasing day by day and to manage such vast amount of information is really a difficult task. The user finds it really hard to capture the desired information as per their need and maximum amount of time is spent in framing proper query and filtering the resultant web pages. The search engine plays a major role in filtering the information and ranking the desired result. The quest for accurate information is still a dream and in this regard this paper presents an approach that tries to optimize the ranking algorithm by employing document clustering and similarity measures. In this paper we present an outline of different ranking algorithms and proposed an approach where PageRank algorithm is optimized by using document clustering. It also employs content mining along with structural mining that help to reduce the computational complexity of the algorithm and thereby diminish the time in performing the ranking of the web pages.
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Irfan, S., Ghosh, S. (2020). Efficient Ranking Framework for Information Retrieval Using Similarity Measure. In: Smys, S., Tavares, J., Balas, V., Iliyasu, A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-37218-7_141
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DOI: https://doi.org/10.1007/978-3-030-37218-7_141
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