Bookmarking Forecast and its Factor Analysis of Social Network Users Based on Gradient Boosting Decision Tree

Abstract

In recent years, the demand for a technology that estimates how much interest users have for an article in social media is growing because the number of marketing cases using social media is increasing. Especially, to distribute the advertisements on social media that match the interests of each user, a technology that forecasts how a user would react/behave for the post when s/he looks at a post are required. This paper develops a method that forecasts whether a user bookmarks a post that s/he would look at, by analyzing the posts that the user has bookmarked in social media based on Gradient Boosting Decision Tree that is one of the ensemble learning methods in artificial intelligence. Moreover, we evaluate the performances of multiple forecast models (XGBoost, LightGBM, and CatBoost), and analyze the factors that cause users to bookmark a post.