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Weighted multi-information constrained matrix factorization for personalized travel location recommendation based on geo-tagged photos

  • Dandan Lyu
  • Ling ChenEmail author
  • Zhenxing Xu
  • Shanshan Yu
Article
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Abstract

Given travel history, travel location recommendation can automatically suggest users where to visit. Huge efforts have been devoted to introducing different additional information (e.g., sequential, textual, geographical, and visual information) for enhancing recommendation performance. However, existing methods only consider limited additional information and treat different information equally. In this paper, we present Weighted Multi-Information Constrained Matrix Factorization (WIND-MF) for personalized travel location recommendation based on geo-tagged photos. On one hand, photos (visual information), users’ visit sequences (sequential information), and textual tags (textual information) are leveraged to comprehensively profile users and travel locations. On the other hand, visual, sequential, and textual similarities as well as geographical distance based co-visit probabilities are assigned with different weights to constrain the factorization of the original user-travel location matrix. We experimented on a dataset of six cities in China, and the experiment results verify the superiority of the proposed method. The code and dataset is available at https://github.com/revaludo/WIND-MF.

Keywords

Geo-tagged photos Personalized travel location recommendation Matrix factorization Multi-information Similarity regularization 

Notes

Acknowledgements

This work is supported by the National Key Research and Development Program of China under Grant No. 2018YFB0505000.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyZhejiang UniversityHangzhouChina

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