کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4946496 | 1439286 | 2017 | 19 صفحه PDF | دانلود رایگان |
- The problem of predicting total sales, to print the right amount of books, is faced.
- A methodology to analyse data related to book sales prediction is proposed.
- A feature selection process is conducted to find out the main factors influencing sales.
- Real world data is analysed using several data mining and visualisation techniques.
- Obtained models are able to predict sales from pre-publication data.
- Predictive models to be used as decision-aid tools for book publishers are presented.
When a new book is launched the publisher faces the problem of how many books should be printed for delivery to bookstores; printing too many is the main issue, since it implies a loss of investment due to inventory excess, but printing too few will also have a negative economic impact. In this paper, we are tackling the problem of predicting total sales in order to print the right amount of books and doing so even before the book has reached the stores. A real dataset including the complete sales data for books published in Spain across several years has been used. We have conducted an analysis in three stages: an initial exploratory analysis, by means of data visualisation techniques; a feature selection process, using different techniques to find out what are the variables that have more impact on sales; and a regression or prediction stage, in which a set of machine learning methods has been applied to create forecasting models for book sales. The obtained models are able to predict sales from pre-publication data with remarkable accuracy, and can be visualised as simple decision trees. Thus, these can be used as decision-aid tools for publishers, which can provide a reliable guidance on the decision process of publishing a book. This is also shown in the paper by addressing four example cases of representative publishers, regarding their number of sales and the number of different books they sell.
Journal: Knowledge-Based Systems - Volume 115, 1 January 2017, Pages 133-151