Abstract
With the rise of online shopping, massive product information continues to emerge, and it becomes a challenge for users to select their favorite things accurately from millions of products. The collaborative filtering algorithm which is widely used could effectively recommend product to users. However, collaborative filtering algorithm only analyzes the relationship between product and user’s evaluation without the analysis of comments. The content contains a lot of useful information and implicates user’s pass judgment about product, so collaborative filtering algorithm with no content analysis would reduce the accuracy of the recommendation results. In this paper, we propose a recommendation algorithm based on the content sentiment analysis and the proposed algorithm improves the performance of the traditional product recommendation algorithm based on collaborative filtering. Experimental results demonstrate that the accuracy of the proposed recommendation algorithm based on sentiment analysis is slightly higher than the recommendation algorithm based on collaborative filtering.
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Yu, J., An, Y., Xu, T., Gao, J., Zhao, M., Yu, M. (2018). Product Recommendation Method Based on Sentiment Analysis. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds) Web Information Systems and Applications. WISA 2018. Lecture Notes in Computer Science(), vol 11242. Springer, Cham. https://doi.org/10.1007/978-3-030-02934-0_45
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DOI: https://doi.org/10.1007/978-3-030-02934-0_45
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