A Serendipity Recommendation Method for Book Categories Using BERT to Strengthen the Web Service of the Book

Abstract

In the field of book search, research on a web service-based user-customized book recommendation system is being conducted to respond to increasingly diverse user requirements. The collaborative filtering algorithm, which is mainly used for book recommendation, has a problem in that it is difficult to reflect the user's recent interest without considering the changes in preference over time, and the user's satisfaction decreases because it repeatedly recommends only similar items. In this paper, we propose a book recommendation method using category similarity based on deep learning. The proposed method is to predict books to be used next time by inputting users' past and current book usage history through BERT, a natural language processing model, and to recommend popular books in other categories with high similarity to the predicted book category in the BERT model to reflect serendipity. This method reflects serendipity, which can lead to users' recent interests and practical preferences, so that recommendation accuracy and user satisfaction can be satisfied at the same time.