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Personalized Recommendation Algorithm Based on Product Reviews

Personalized Recommendation Algorithm Based on Product Reviews

Zhibo Wang, Mengyuan Wan, Xiaohui Cui, Lin Liu, Zixin Liu, Wei Xu, Linlin He
Copyright: © 2018 |Volume: 16 |Issue: 3 |Pages: 17
ISSN: 1539-2937|EISSN: 1539-2929|EISBN13: 9781522542384|DOI: 10.4018/JECO.2018070103
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MLA

Wang, Zhibo, et al. "Personalized Recommendation Algorithm Based on Product Reviews." JECO vol.16, no.3 2018: pp.22-38. http://doi.org/10.4018/JECO.2018070103

APA

Wang, Z., Wan, M., Cui, X., Liu, L., Liu, Z., Xu, W., & He, L. (2018). Personalized Recommendation Algorithm Based on Product Reviews. Journal of Electronic Commerce in Organizations (JECO), 16(3), 22-38. http://doi.org/10.4018/JECO.2018070103

Chicago

Wang, Zhibo, et al. "Personalized Recommendation Algorithm Based on Product Reviews," Journal of Electronic Commerce in Organizations (JECO) 16, no.3: 22-38. http://doi.org/10.4018/JECO.2018070103

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Abstract

Under the background of leap-forward development for the internet, e-commerce has played an important role in people's daily life, but huge data sizes have also brought problems, such as information overload which can be solved by using a recommendation system effectively. However, with the development of the e-commerce, the amount of the product catalogs and users becomes larger, which causes lower performance of the traditional recommendation system. This article comes up with a personalized recommendation algorithm based on the data mining of product reviews to optimize the performance of the new recommendation system. Features of the product were extracted, for which the users' sentiment polarity was analyzed. This article develops a recommendation system based on the user's preference model and the product features to get the recommendation result. Experimental results show that a personalized recommendation has significantly improved the accuracy and recall rate when compared with a traditional recommendation algorithm.

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