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Music Therapy Counseling Recommendation Model Based on Collaborative Filtering

협업 필터링 기반의 음악 치료 상담 추천 모델

  • Park, Seong-Hyun (Dept. of Computer Engineering, Kongju National University) ;
  • Kim, Jae-Woong (Dept. of Computer Science & Engineering, Kongju National University) ;
  • Kim, Dong-Hyun (Dept. of Computer Engineering, Kongju National University) ;
  • Cho, Han-Jin (Dept. of Energy IT Engineering, Far East University)
  • 박성현 (공주대학교 컴퓨터공학과) ;
  • 김재웅 (공주대학교 컴퓨터공학부) ;
  • 김동현 (공주대학교 컴퓨터공학과) ;
  • 조한진 (극동대학교 에너지IT공학과)
  • Received : 2019.08.02
  • Accepted : 2019.09.20
  • Published : 2019.09.28

Abstract

Music therapy, a field that convergence music and treatment, which play a fundamental role in personality formation, possesses diverse and complex treatment methods. Music therapists in charge of music therapy may experience the same phenomenon as countertransference in consultation with clients. In addition, experiencing psychological burnout, there are many difficulties in reaching the final goal of music therapy. In this paper, we provide a collaborative filtering-based music therapy consultation data recommendation model for smooth music therapy consultation with clients who visited for music therapy. The proposed model grasps the similarity between the conventional consultation data and the new consultant data through the euclidean distance algorithm. This is to recommend similar consultation materials. Since music therapists can provide optimal consultation materials for consultants who need music therapy, smooth consultation is expected.

전인적인 인격 형성에 근본적인 역할을 하고 있는 음악과 치료가 융합된 분야인 음악치료는 다양하고 복잡한 치료 방법을 가지고 있다. 음악치료를 담당하고 있는 음악치료사들은 내담자와의 상담에 역전이와 같은 경우의 현상이 발생하기도 하며, 심리적 소진을 경험하고 있기에, 음악 치료의 최종 목표 도달에 많은 어려움이 발생하고 있는 상황이다. 본 논문에서는 음악치료를 위하여 방문한 내담자와의 원활한 음악 치료 상담을 위하여 협업 필터링 기반의 음악치료 상담 자료 추천 모델을 제안한다. 제안 모델은 기존 상담 데이터와 새로운 상담자의 데이터를 유클리디안 거리 알고리즘을 통하여 유사도를 파악하고, 이를 통하여 유사 상담 자료를 추천하는 것으로서, 음악치료사들은 음악 치료가 필요한 상담자에게 가장 적합한 상담 자료를 제공할 수 있기에 원활한 상담이 진행될 수 있을 것으로 기대된다.

Keywords

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