Developing a hybrid collaborative filtering recommendation system with opinion mining on purchase review

Youdong Yun, Danial Hooshyar, Jaechoon Jo, Heuiseok Lim

Research output: Contribution to journalArticle

22 Citations (Scopus)

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?’.

Original languageEnglish
Pages (from-to)331-344
Number of pages14
JournalJournal of Information Science
Volume44
Issue number3
DOIs
Publication statusPublished - 2018 Jun 1

Keywords

  • Collaborative filtering
  • hybrid recommendation system
  • opinion mining
  • purchase review

ASJC Scopus subject areas

  • Information Systems
  • Library and Information Sciences

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