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Cross-Domain Recommendation for Mapping Sentiment Review Pattern

  • Yang Xu
  • Zhaohui Peng
  • Yupeng Hu
  • Xiaoguang Hong
  • Wenjing Fu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11061)

Abstract

Cross-domain algorithms which aim to transfer knowledge available in the source domains to the target domain are gradually becoming more attractive as an effective approach to help improving quality of recommendations and to alleviate the problems of cold-start and data sparsity in recommendation systems. However, existing works on cross-domain algorithm mostly consider ratings, tags and the text information like reviews, cannot use the sentiments implicated in the reviews efficiently. In this paper, we propose a Sentiment Review Pattern Mapping framework for cross-domain recommendation, called SRPM. The proposed SRPM framework can model the semantic orientation of the reviews of users, and transfer sentiment review pattern of users by using a multi-layer perceptron to capture the nonlinear mapping function across domains. We evaluate and compare our framework on a set of Amazon datasets. Extensive experiments on each cross-domain recommendation scenarios are conducted to prove the high accuracy of our proposed SRPM framework.

Keywords

Cross-domain recommendation Sentiment review pattern Pattern mapping 

Notes

Acknowledgments

This work is supported by NSF of Shandong, China (Nos. ZR2017MF065, ZR2018MF014), the Science and Technology Development Plan Project of Shandong, China (No. 2016GGX101034).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yang Xu
    • 1
  • Zhaohui Peng
    • 1
  • Yupeng Hu
    • 1
  • Xiaoguang Hong
    • 1
  • Wenjing Fu
    • 1
  1. 1.School of Computer Science and TechnologyShandong UniversityJinanPeople’s Republic of China

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