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펭소니 ( Sony Peng ),싯소포호트 ( Sophort Siet ),일홈존 ( Sadriddinov Ilkhomjon ),김대영 ( Daeyoung Kim ),박두순 ( Doo-soon Park ) 한국정보처리학회 2023 한국정보처리학회 학술대회논문집 Vol.30 No.2
With the explosion of information, recommendation algorithms are becoming increasingly important in providing people with appropriate content, enhancing their online experience. In this paper, we propose a recommender system using advanced deep reinforcement learning(DRL) techniques. This method is more adaptive and integrative than traditional methods. We selected the MovieLens dataset and employed the precision metric to assess the effectiveness of our algorithm. The result of our implementation outperforms other baseline techniques, delivering better results for Top-N item recommendations.
잠재적 소셜 네트워크를 이용하여 스펙트럼 분할하는 방식 기반 영화 추천 시스템
일홈존 ( Sadriddinov Ilkhomjon ),펭소니 ( Sony Peng ),싯소포호트 ( Sophort Siet ),김대영 ( Dae-young Kim ),박두순 ( Doo-soon Park ) 한국정보처리학회 2023 한국정보처리학회 학술대회논문집 Vol.30 No.2
We propose a method of movie recommendation that involves an algorithm known as spectral bipartition. The Social Network is constructed manually by considering the similar movies viewed by users in MovieLens dataset. This kind of similarity establishes implicit ties between viewers. Because we assume that there is a possibility that there might be a connection between users who share the same set of viewed movies. We cluster users by applying a community detection algorithm based on the spectral bipartition. This study helps to uncover the hidden relationships between users and recommend movies by considering that feature.
POI 에서 딥러닝을 이용한 개인정보 보호 추천 시스템
펭소니 ( Sony Peng ),박두순 ( Doo-soon Park ),김대영 ( Daeyoung Kim ),양예선 ( Yixuan Yang ),이혜정 ( Hyejung Lee ),싯소포호트 ( Sophort Siet ) 한국정보처리학회 2022 한국정보처리학회 학술대회논문집 Vol.29 No.2
POI refers to the point of Interest in Location-Based Social Networks (LBSNs). With the rapid development of mobile devices, GPS, and the Web (web2.0 and 3.0), LBSNs have attracted many users to share their information, physical location (real-time location), and interesting places. The tremendous demand of the user in LBSNs leads the recommendation systems (RSs) to become more widespread attention. Recommendation systems assist users in discovering interesting local attractions or facilities and help social network service (SNS) providers based on user locations. Therefore, it plays a vital role in LBSNs, namely POI recommendation system. In the machine learning model, most of the training data are stored in the centralized data storage, so information that belongs to the user will store in the centralized storage, and users may face privacy issues. Moreover, sharing the information may have safety concerns because of uploading or sharing their real-time location with others through social network media. According to the privacy concern issue, the paper proposes a recommendation model to prevent user privacy and eliminate traditional RS problems such as cold-start and data sparsity.
클리크 마이닝에 기반한 새로운 커뮤니티 탐지 알고리즘 연구
양예선 ( Yixuan Yang ),펭소니 ( Sony Peng ),박두순 ( Doo-soon Park ),김석훈 ( Seok-hoon Kim ),이혜정 ( Hyejung Lee ),싯소포호트 ( Sophort Siet ) 한국정보처리학회 2022 한국정보처리학회 학술대회논문집 Vol.29 No.2
Community detection is meaningful research in social network analysis, and many existing studies use graph theory analysis methods to detect communities. This paper proposes a method to detect community by detecting maximal cliques and obtain the high influence cliques by high influence nodes, then merge the cliques with high similarity in social network.