Book Recommender System

Abstract

A recommender system is an application that analyses data and makes recommendations for things that a user might be interested in. For example, the Book Recommender System uses user information to suggest relevant books. However, many of the systems do not include recommendations based on the interests and backgrounds of other readers. Therefore, this study proposes a recommender system model for book recommendations that incorporates the background and interests of other readers. This data was combined with the reader's information to generate a list of books that would be of interest to the reader. Furthermore, the system has a comment section that allows users to provide input on the book, whether they like or detest it. The system's development is split into two parts: a user interest survey and prototype development. The purpose of the survey was to gather information on the background and interests of book readers. This data serves as the initital data for the recommender system. The Waterfall model is used in the construction of the recommender system. The proposed recommender system is critical in assisting readers in finding and selecting books that are relevant to their interests. This will save the reader a significant amount of time when browsing the book's collection. Past readers' comments will provide a general summary of the book. This will aid the reader in deciding whether or not to continue reading. The system was tested on a group of readers who served as the study's respondents. The findings shows that more than 80% of the respondents were generally satisfied (agree and strongly agree) with the system's interface design and content.