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Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering

Annals of Computer Science and Information Systems, Volume 33

Utilize Deep learning to increase the performance of a Book recommender system using the Item-based Collaborative Filtering

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DOI: http://dx.doi.org/10.15439/2022R11

Citation: Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering, Vu Dinh Khoa, Shivani Agarwal, Gloria Jeanette Rincon Aponte, Nguyen Thi Hong Nga, Vijender Kumar Solanki, Ewa Ziemba (eds). ACSIS, Vol. 33, pages 109113 ()

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Abstract. Item-based Collaborative Filtering is a common and efficient approach for recommendation problems. In this study, we have investigated the power of deep learning in textual feature extraction and applied this advantage to a high-performance item-based collaborative filtering recommender system. The proposed approach has been experienced on book datasets added by texts collected from famous book review sites. The experiment proves that the proposed model has better performance thanks to the contribution of the new item profile process method based on Deep Learning.


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