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Book Recommendation System (BRS) Using Collaboration Filtering Machine Learning
The project is about the exploration of the world of book recommendation system with the primary objective of delivering personalized book recommendations to an extensive audience of book passions and readers. Firstly, the project will give the significance of recommendation system and their widespread application. In where it outlines the research objectives which emphasizing the development and deployment of an effective BRS. Hence, it will be discussed into the data preprocessing phases where the project prepares and cleaned the four dataset that will be provided. Also, various of data preprocessing techniques are used including handling missing values, outliers’ detection and data standardization. Then, it will be discussed on the creation and evaluation of three recommendation model which are K-Nearest Neighbors (KNN), Support Vector Classification (SVC) and Neural Collaborative Filtering (NCF). The model is assessed based on performance metrics such as Mean Squared Error (MSE) and Root Mean Square Error (RMSE). The comparative analysis informs the selection of the most effective model for deployment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Springer Science and Business Media Deutschland GmbH
Studies in Systems, Decision and Control
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