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Leveraging Similar Reviews to Discover What Users Want

  • Zongze Jin
  • Yun ZhangEmail author
  • Weimin Mu
  • Weiping Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11305)

Abstract

With the development of deep learning techniques, recommender systems leverage deep neural networks to extract both the features of users and items, which have achieved great success. Most existing approaches leverage both the descriptions and reviews to represent the features of an item. However, for some items, such as newly released products, they lack users’ reviews. In this case, only the descriptions of these items can be used to represent their features, which may result in bad representations of these items and further influence the performance of recommendations. In this paper, we present a deep learning based framework, which can use the reviews of the items that are similar to the target items to complement the descriptions. At last, we do experiments on three real world datasets and the results demonstrate that our model outperforms the state-of-the-art methods.

Keywords

Recommendation system Deep learning Knowledge representation 

Notes

Acknowledgment

This task was supported by National Key Research and Development Plan (2016QY02D0402).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Zongze Jin
    • 1
    • 2
  • Yun Zhang
    • 1
    • 2
    Email author
  • Weimin Mu
    • 1
    • 2
  • Weiping Wang
    • 2
    • 3
  1. 1.School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina
  2. 2.Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  3. 3.National Engineering Research Center Information Security Common Technology, NERCISBeijingChina

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