Developing a hybrid collaborative filtering recommendation system with opinion mining on purchase review
- Authors
- Yun, Youdong; Hooshyar, Danial; Jo, Jaechoon; Lim, Heuiseok
- Issue Date
- Jun-2018
- Publisher
- SAGE PUBLICATIONS LTD
- Keywords
- Collaborative filtering; hybrid recommendation system; opinion mining; purchase review
- Citation
- JOURNAL OF INFORMATION SCIENCE, v.44, no.3, pp.331 - 344
- Indexed
- SCIE
SSCI
SCOPUS
- Journal Title
- JOURNAL OF INFORMATION SCIENCE
- Volume
- 44
- Number
- 3
- Start Page
- 331
- End Page
- 344
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/75396
- DOI
- 10.1177/0165551517692955
- ISSN
- 0165-5515
- Abstract
- The most commonly used algorithm in recommendation systems is collaborative filtering. However, despite its wide use, the prediction accuracy of this algorithm is unexceptional. Furthermore, whether quantitative data such as product rating or purchase history reflect users' actual taste is questionable. In this article, we propose a method to utilise user review data extracted with opinion mining for product recommendation systems. To evaluate the proposed method, we perform product recommendation test on Amazon product data, with and without the additional opinion mining result on Amazon purchase review data. The performances of these two variants are compared by means of precision, recall, true positive recommendation (TPR) and false positive recommendation (FPR). In this comparison, a large improvement in prediction accuracy was observed when the opinion mining data were taken into account. Based on these results, we answer two main questions: 'Why is collaborative filtering algorithm not effective?' and 'Do quantitative data such as product rating or purchase history reflect users' actual tastes?'
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- Appears in
Collections - Graduate School > Department of Computer Science and Engineering > 1. Journal Articles
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