Earticle

초록

영어

Online product reviews have been an important source for customers to make informed decisions when purchasing goods. Yet, it is nearly impossible for consumers to access all the available reviews online. Such problem could be overcome by employing a recommendation system. Collaborative filtering (CF) recommendation system recommends products based on users’ ratings which may not represent customers’ true opinions on the items they bought. In this study, ratings were substituted with those computed using the frequencies of positive and negative words and expressions obtained from product reviews when developing a sentiment-based recommendation system. The objective of this study is to compare three recommendation systems: traditional CF-based recommendation, sentiment-based recommendation utilizing publicly available lexicon, sentiment-based recommendation employing domain-specific words and expressions examined in the study. The experiments conducted using the data obtained from MakeupAlley.com indicated that sentiment-based recommendation system applying domain-specific words and expressions outperformed the other two systems.

목차

Abstract
 Introduction
 Literature
  Collaborative Filtering (CF) Recommendation System
  Sentiment Analysis
 Methods
  Overall Framework
  Data Description
  Creating Domain-Specific Words & Expressions
  Creating Recommendations by CF Approach
 Experiments
  Experimental Design
  Results
 Discussion
 References

키워드

저자

• Heejin Yang [ Business School, Korea University, Anam-Ro 145, Seongbuk-Gu, Seoul 136-701, Republic of Korea ]
• Yongmoo Suh [ Business School, Korea University, Anam-Ro 145, Seongbuk-Gu, Seoul 136-701, Republic of Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2025

• 십진분류

  KDC 325 DDC 658