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Geographical Relevance Model for Long Tail Point-of-Interest Recommendation

  • Wei Liu
  • Zhi-Jie Wang
  • Bin Yao
  • Mengdie Nie
  • Jing Wang
  • Rui Mao
  • Jian Yin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)

Abstract

Point-of-Interest (POI) recommendation plays a key role in people’s daily life, and has been widely studied in recent years, due to its increasingly applications (e.g., recommending new restaurants for users). One of important phenomena in the POI recommendation community is the data sparsity, which makes deep impact on the quality of recommendation. Existing works have proposed various models to alleviate the bottleneck of the data sparsity, and most of these works addressed this issue from the user perspective. To the best of our knowledge, few attention has been made to address this issue from the POI perspective. In this paper, we observe that the “long tail” POIs, which have few check-ins and have less opportunity to be exposed, take up a great proportion among all the POIs. It is interesting and meaningful to investigate the long tail POI recommendation from the POI perspective. To this end, this paper proposes a new model, named GRM (geographical relevance model), that expands POI profiles via relevant POIs and employs the geographical information, addressing the limitations of existing models. Experimental results based on two public datasets demonstrate that our model is effective and competitive. It outperforms state-of-the-art models for the long tail POI recommendation problem.

Keywords

Long tail Relevance model Geographical information Point-of-interest recommendation 

Notes

Acknowledgment

This work was supported by the NSFC (61472453,61672351, 61729202, 91438121, U1401256, U1501252, U1611264, U1711261, U1636210 and U1711262), the Opening Projects of Guangdong Key Laboratory of Big Data Analysis and Processing (201808), Guangdong Province Key Laboratory of Popular High Performance Computers of Shenzhen University (SZU-GDPHPCL2017), State Key Laboratory of Mathematical Engineering and Advanced Computing (2017A01), the 2016 Characteristic Innovation Project (Natural Science) of Education Department of Guangdong Province of China (2016KTSCX162), Foshan Science and Technology Bureau Project (2016AG100382), and Guangdong Pre-national project (2014GKXM054).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Data and Computer ScienceSun Yat-Sen UniversityGuangzhouChina
  2. 2.Shanghai Jiao Tong UniversityShanghaiChina
  3. 3.Neusoft Institute GuangdongFoshanChina
  4. 4.Shenzhen UniversityShenzhenChina
  5. 5.Guangdong Key Laboratory of Big Data Analysis and ProcessingGuangzhouChina
  6. 6.Guangdong Province Key Laboratory of Popular High Performance ComputersShenzhenChina

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