Abstract
Large data repositories helped us in support systems but created a huge problem for meaningful information retrieval. Filtering of data based on user requirements solved this problem. This process of data filtering when combined with prediction developed recommendation systems. Initial work in recommendation systems can be listed in the areas of cognitive science, approximation theory, marketing models, and automatic text processing. This paper focuses on recommendation system for books. In this paper, training and testing models are designed to predict user ratings for new users. The predicted user ratings are used to propose three types of recommendations based on three different user attributes.
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Acknowledgements
We sincerely thank Mr. Cai-Nicolas Ziegler and Book-Crossing community for collection of dataset. This data is freely available for research and we acknowledge the hard work done in the collection of data [18].
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Rohit, Sabitha, S., Choudhury, T. (2018). Proposed Approach for Book Recommendation Based on User k-NN. In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds) Advances in Computer and Computational Sciences. Advances in Intelligent Systems and Computing, vol 554. Springer, Singapore. https://doi.org/10.1007/978-981-10-3773-3_53
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DOI: https://doi.org/10.1007/978-981-10-3773-3_53
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