Abstract
The Internet platform has been gaining increased popularity since the internet platform is a storehouse of knowledge that is extremely immense and varied. The size of the internet increases every day and more and more content is being added to the platform, which makes it difficult to access the information that is required. Therefore, various search engines are developed that enable searching for relevant webpages according to the query provided. The search results are mostly generalized and the results are fetched every single time the query is fired, which increases the load on the search engine which is not personalized according to the user. There is an immense need to personalize the web platform as it is beneficial to the provider as well as the user in achieving a streamlined approach that can be set according to the user's preferences and needs. Therefore, to improve this situation and provide a definitive solution to the web personalization problem, this publication utilizes the Entropy Estimation and Cosine Similarity to automatically personalize the content for the query passed enhancing the personal user experience significantly.
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Asabe, S.H., Suryawanshi, A., Joshi, V., Abhichandan, D., Jain, G. (2021). Efficient Management of Web Personalization Through Entropy and Similarity Analysis. In: Swain, D., Pattnaik, P.K., Athawale, T. (eds) Machine Learning and Information Processing. Advances in Intelligent Systems and Computing, vol 1311. Springer, Singapore. https://doi.org/10.1007/978-981-33-4859-2_9
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DOI: https://doi.org/10.1007/978-981-33-4859-2_9
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