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Product Recommendation Method Based on Sentiment Analysis

  • Jian Yu
  • Yongli An
  • Tianyi Xu
  • Jie Gao
  • Mankun Zhao
  • Mei YuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11242)

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.

Keywords

Collaborative filtering algorithm Sentiment analysis Product recommendation algorithm 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jian Yu
    • 1
    • 2
    • 3
  • Yongli An
    • 1
    • 2
    • 3
  • Tianyi Xu
    • 1
    • 2
    • 3
  • Jie Gao
    • 1
    • 2
    • 3
  • Mankun Zhao
    • 1
    • 2
    • 3
  • Mei Yu
    • 1
    • 2
    • 3
    Email author
  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina
  2. 2.Tianjin Key Laboratory of Advanced NetworkingTianjinChina
  3. 3.Tianjin Key Laboratory of Cognitive Computing and ApplicationTianjinChina

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