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| Books' Rating Prediction Using Just NN |
Alaa Mazen Maghari, Iman Ali Al-Najjar, Said Jamil Al-laqtah
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Abstract:
The aim behind analyzing the Goodreads dataset is to get a fair idea about the relationships between the multiple attributes a book might have, such as: the aggregate rating of each book, the trend of the authors over the years and books with numerous languages. With over a hundred thousand ratings, there are books which just tend to become popular as each day seems to pass. We proposed an Artificial Neural Network (ANN) model for predicting the overall rating of books. The prediction is based on these features (bookID, title, authors, isbn, language_code, isbn13, # num_pages, ratings_count, text_reviews_count), which were used as input variables and (average_rating) as output variable for our ANN model. Our model were created, trained, and validated using data set in JNN environment, which its title is "Goodreads-books". Model evaluation showed that the ANN model is able to predict correctly 99.78% of the validation samples.
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