Abstract
One of the major challenges face by webmasters is the introduction of numerous choices to the customer, which leads to repetitive and difficulty in locating the correct item or data on the webpage. In the traditional approach, if the data was changed, pooling approach was possible, only if data variation was within the cluster information. In case the data exceeds the limit, classification was difficult to perform. Therefore, we need to have a classification approach that can work under these conditions. In the proposed work we have implemented Hybrid of ANN and KNN approach and found improvement in the recommendation system with greater accuracy.
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Kaur, J., Bal, J.S. (2020). Machine Learning Approach to Recommender System for Web Mining. In: Hemanth, D., Shakya, S., Baig, Z. (eds) Intelligent Data Communication Technologies and Internet of Things. ICICI 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-030-34080-3_72
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DOI: https://doi.org/10.1007/978-3-030-34080-3_72
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