Abstract
Exponential growth of web accesses on the Internet causes substantial delays in providing services to the user. Web prefetching is an effective solution that can improve the performance of the web by reducing the latency perceived by the user. Content on the web page also provides meaningful data to predict the future requests. This paper presents a content-based semantic prefetching approach. The proposed approach basically works on the semantic preferences of the tokens present in the anchor text associated with the URLs. To make more accurate predictions, it also uses the semantic information which is explicitly embedded with each link. It then computes the semantic association between the tokens and links then associates weightage in order to improve the prediction accuracy. This prefetching scheme would be more effective for long browsing sessions and will achieve good hit rate.
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Setia, S., Jyoti, Duhan, N. (2018). A Novel Approach for Semantic Prefetching Using Semantic Information and Semantic Association. In: Aggarwal, V., Bhatnagar, V., Mishra, D. (eds) Big Data Analytics. Advances in Intelligent Systems and Computing, vol 654. Springer, Singapore. https://doi.org/10.1007/978-981-10-6620-7_45
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DOI: https://doi.org/10.1007/978-981-10-6620-7_45
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