사용자의 정성적 선호도와 정량적 선호도를 고려하는 추천 시스템 성능 향상에 관한 연구

With the recent rapid development of ICT (Information and Communication Technology) and mobile devices, most users receive various types of information. Thus, users would face information overload issues, which takes much time to select products and services they need or prefer. Therefore, a personalized recommender system has become a practical methodology to address such issues. Existing studies mainly utilized quantitative preferences (e.g., star ratings, click). However, such methodology has limitations in that quantitative information can not fully reflect the user's preference. Therefore, we proposed a novel recommender system methodology that utilized quantitative and qualitative preferences information. To evaluate the performance of the proposed methodology we collected the real-world dataset that contains 771,824 reviews, 648,210 users, and 470 hotels on Tripadvisor.com. The performance of the proposed methodology using quantitative and qualitative preferences information showed better performance than quantitative preferences.