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Ali Mansourian

Ali Mansourian

Professor

Ali Mansourian

A Personalized location-based and serendipity-oriented point of interest recommender assistant based on behavioral patterns

Author

  • Samira Khoshahval
  • Mahdi Farnaghi
  • Mohammad Taleai
  • Ali Mansourian

Editor

  • Ali Mansourian
  • Petter Pilesjö
  • Lars Harrie
  • Ron van Lammeren

Summary, in English

The technological evolutions have promoted mobile devices from rudimentary communication facilities to advanced personal assistants. According to the huge amount of accessible data, developing a time-saving and cost-effective method for location-based recommendations in mobile devices has been considered a challenging issue. This paper contributes a state-of-the-art solution for a personalized recommender assistant which suggests both accurate and unexpected point of interests (POIs) to users in each part of the day of the week based on their previously monitored, daily behavioral patterns. The presented approach consists of two steps of extracting the behavioral patterns from users’ trajectories and location-based recommendation based on the discovered patterns and user’s ratings. The behavioral pattern of the user includes their activity types in different parts of the day of the week, which is monitored via a combination of a stay point detection algorithm and an association rule mining (ARM) method. Having the behavioral patterns, the system exploits two recommendation procedures based on conventional collaborative filtering and K-furthest neighborhood model to recommend typical and serendipitous POIs to the users. The suggested POI list contains not only relevant and precise POIs but also unpredictable and surprising items to the users. To evaluate the system, the values of RMSE of each procedure were computed and compared. Conducted experiments proved the feasibility of the proposed solution.

Department/s

  • Dept of Physical Geography and Ecosystem Science
  • MECW: The Middle East in the Contemporary World
  • Middle Eastern Studies

Publishing year

2018-01-01

Language

English

Pages

271-289

Publication/Series

Lecture Notes in Geoinformation and Cartography

Volume

part F3

Document type

Conference paper

Publisher

Springer International Publishing

Topic

  • Other Computer and Information Science
  • Physical Geography

Keywords

  • Association rule mining
  • Behavioral pattern
  • K-furthest neighborhood
  • Personalized recommender assistant
  • Point of interest (POI)
  • Serendipity

Conference name

21st AGILE Conference on Geographic Information Science, 2018

Conference date

2018-06-12 - 2018-06-15

Conference place

Lund, Sweden

Status

Published

ISBN/ISSN/Other

  • ISSN: 1863-2351
  • ISSN: 1863-2246
  • ISBN: 978-3-319-78208-9
  • ISBN: 9783319782072