Objective Book Recommender System
Name
Roman Šumailov
Abstract
This work’s goal was the creation of a book recommender system based on objectively measurable attributes. For recommendations, the created system uses linear regression model trained on the books’ ratings. In this work effectiveness of the described approach as a solution for the given problem is measured. The model is able to estimate the differences between two books in English. For every book’s content, a set of attributes describing the book are calculated, such as text positivity or average sentence length. Books’ ratings and open books collection were used as the training dataset. As a result, the model is able to find differences correctly in terms of the training data provided, but is not able to correctly find the nearest books from human point of view due to the lack of a large good quality dataset. Based on the model’s work correctness, it was concluded that given a big enough good quality training dataset the approach used in this work has potential to find a model capable of finding the nearest books in human understanding.
Graduation Thesis language
Estonian
Graduation Thesis type
Bachelor - Computer Science
Supervisor(s)
Siim Karus
Defence year
2015