Earticle

초록

영어

This study proposes a novel context-aware recommender system, which is designed to recommend the items according to the customer’s responses to the previously recommended item. In specific, our proposed system predicts the user’s emotional state from his or her responses (such as facial expressions and movements) to the previous recommended item, and then it recommends the items that are similar to the previous one when his or her emotional state is estimated as positive. If the customer’s emotional state on the previously recommended item is regarded as negative, the system recommends the items that have characteristics opposite to the previous item. Our proposed system consists of two sub modules-(1) emotion prediction module, and (2) responsive recommendation module. Emotion prediction module contains the emotion prediction model that predicts a customer’s arousal level-a physiological and psychological state of being awake or reactive to stimuli-using the customer’s reaction data including facial expressions and body movements, which can be measured using Microsoft’s Kinect Sensor. Responsive recommendation module generates a recommendation list by using the results from the first module-emotion prediction module. If a customer shows a high level of arousal on the previously recommended item, the module recommends the items that are most similar to the previous item. Otherwise, it recommends the items that are most dissimilar to the previous one. In order to validate the performance and usefulness of the proposed recommender system, we conducted empirical validation. In total, 30 undergraduate students participated in the experiment. We used 100 trailers of Korean movies that had been released from 2009 to 2012 as the items for recommendation. For the experiment, we manually constructed Korean movie trailer DB which contains the fields such as release date, genre, director, writer, and actors. In order to check if the recommendation using customers’ responses outperforms the recommendation using their demographic information, we compared them. The performance of the recommendation was measured using two metrics-satisfaction and arousal levels. Experimental results showed that the recommendation using customers’ responses (i.e. our proposed system) outperformed the recommendation using their demographic information with statistical significance.

목차

Abstract
 1. 서론
 2. 이론적 배경
  2.1 감정 예측
  2.2 추천시스템
  2.3 상황인지 추천시스템
 3. 제안 시스템 구성 체계
  3.1 감정 예측모형
  3.2 반응형 추천모형
 4. 제안 시스템 구현 및 검증
  4.1 감정 예측모듈의 구현
  4.2 반응형 추천모듈의 구현
  4.3 유용성 검증을 위한 실험설계 및 결과
 5. 결언
 참고문헌

키워드

저자

• 안현철 [ Hyunchul Ahn | 국민대학교 경영대학 경영정보학부 부교수 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JITAM [Journal of Information Technology Applications and Management]

• 간기

  격월간

• pISSN

  1598-6284

• eISSN

  2508-1209

• 수록기간

  1999~2026

• 십진분류

  KDC 005 DDC 005