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http://dx.doi.org/10.6109/jkiice.2020.24.8.970

Item-Based Collaborative Filtering Recommendation Technique Using Product Review Sentiment Analysis  

Yun, So-Young (Information & Computer Center, Pukyong National University)
Yoon, Sung-Dae (Department of Computer Engineering, Pukyong National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.8, 2020 , pp. 970-977 More about this Journal
Abstract
The collaborative filtering recommendation technique has been the most widely used since the beginning of e-commerce companies introducing the recommendation system. As the online purchase of products or contents became an ordinary thing, however, recommendation simply applying purchasers' ratings led to the problem of low accuracy in recommendation. To improve the accuracy of recommendation, in this paper suggests the method of collaborative filtering that analyses product reviews and uses them as a weighted value. The proposed method refines product reviews with text mining to extract features and conducts sentiment analysis to draw a sentiment score. In order to recommend better items to user, sentiment weight is used to calculate the predicted values. The experiment results show that higher accuracy can be gained in the proposed method than the traditional collaborative filtering.
Keywords
Recommendation Technique; Collaborative Filtering; Text Mining; Sentiment Analysis;
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