Location
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
Document Type
Event
Start Date
24-11-2025 4:00 PM
Description
When selecting a book, readers often rely on surface level information such as the title, author, synopsis, and keywords to determine whether a story matches their interests. These features contain important cues related to genre, tone, and narrative elements that help set expectations before reading. The Intelligent Book Recommendation and Rating Prediction System works to automate this process by using natural language processing and machine learning techniques. It takes in readers’ personalized reading data such as book titles, author, subjects, synopsis, and personal ratings to learn semantic patterns using TF-IDF vectorization. A supervised Linear Regression model was then trained to analyze these patterns and learn the relationship between the natural language and the readers’ personal ratings. The model allows the reader to input an ISBN13 and predict how the reader will rate the book given the information collected from the ISBNdb API. Experimental results show that textual metadata can predict personal ratings with meaningful accuracy. Furthermore, results suggest that prediction accuracy increases when the training dataset contains a wider and more varied set of books, reinforcing the importance of data diversity in personalized models.
Included in
GRM-20188 Intelligent Book Recommendation and Rating Prediction System
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
When selecting a book, readers often rely on surface level information such as the title, author, synopsis, and keywords to determine whether a story matches their interests. These features contain important cues related to genre, tone, and narrative elements that help set expectations before reading. The Intelligent Book Recommendation and Rating Prediction System works to automate this process by using natural language processing and machine learning techniques. It takes in readers’ personalized reading data such as book titles, author, subjects, synopsis, and personal ratings to learn semantic patterns using TF-IDF vectorization. A supervised Linear Regression model was then trained to analyze these patterns and learn the relationship between the natural language and the readers’ personal ratings. The model allows the reader to input an ISBN13 and predict how the reader will rate the book given the information collected from the ISBNdb API. Experimental results show that textual metadata can predict personal ratings with meaningful accuracy. Furthermore, results suggest that prediction accuracy increases when the training dataset contains a wider and more varied set of books, reinforcing the importance of data diversity in personalized models.