@article{MA29CA7AD,
title = "Personalized Cross-Domain Recommendation of Books Based on Video Consumption Data",
journal = "The Transactions of the Korea Information Processing Society",
year = "2024",
issn = "null",
doi = "https://doi.org/10.3745/TKIPS.2024.13.8.382",
author = "Yea Bin Lim, Hyon Hee Kim",
keywords = "Cross Domain, Hybrid Recommendation System, Word2Vec, Personalization, Deep Learning",
abstract = "Recently, the amount of adult reading has been continuously decreasing, but the consumption of video content is increasing. 
Accordingly, there is no information on preferences and behavior patterns for new users, and user evaluation or purchase of new
books are insufficient, causing cold start problems and data scarcity problems. In this paper, a hybrid book recommendation system 
based on video content was proposed. The proposed recommendation system can not only solve the cold start problem and data scarcity 
problem by utilizing the contents of the video, but also has improved performance compared to the traditional book recommendation 
system, and even high-quality recommendation results that reflect genre, plot, and rating information-based user taste information 
were confirmed."
}