Year: 2025
Type: article
Abstract: Digital systems record data on the books we read, items we purchase, or the products we enjoy, all within their own framework for the purpose of data mining. The information to be derived from these data through data mining methods is of great importance for these systems. Data mining aims to speed up the decision-making process by uncovering hidden relationships and information in data via mathematical and statistical techniques. In this context, data mining techniques were applied to the dataset containing book votes through the use of the Apriori algorithm in this study. In this way, the relationships within the data set were identified, rules were generated, and the connections between the generated rules and user preferences were disclosed. With the application of the Apriori algorithm, rules were generated that show books associated with other books, as well as authors often liked together with other authors. The study developed rules indicating which other books a reader might enjoy based on their interest in a particular book, as well as which authors might appeal to readers who like a given author. The rules produced in the study are intended to be used in book recommendation lists and sales strategies.
Authors: Merve Köle, Emin Borandag
Institution: Manisa Celal Bayar University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7117114318