A Personalized location-based and serendipity-oriented point of interest recommender assistant based on behavioral patterns
(2018) 21st AGILE Conference on Geographic Information Science, 2018 In Lecture Notes in Geoinformation and Cartography part F3. p.271-289- Abstract
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... (More)
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.
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- author
- Khoshahval, Samira ; Farnaghi, Mahdi LU ; Taleai, Mohammad and Mansourian, Ali LU
- organization
- publishing date
- 2018-01-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Association rule mining, Behavioral pattern, K-furthest neighborhood, Personalized recommender assistant, Point of interest (POI), Serendipity
- host publication
- Geospatial Technologies for All : Selected Papers of the 21st AGILE Conference on Geographic Information Science - Selected Papers of the 21st AGILE Conference on Geographic Information Science
- series title
- Lecture Notes in Geoinformation and Cartography
- editor
- Mansourian, Ali ; Pilesjö, Petter ; Harrie, Lars and van Lammeren, Ron
- volume
- part F3
- pages
- 19 pages
- publisher
- Springer International Publishing
- conference name
- 21st AGILE Conference on Geographic Information Science, 2018
- conference location
- Lund, Sweden
- conference dates
- 2018-06-12 - 2018-06-15
- external identifiers
-
- scopus:85044825370
- ISSN
- 1863-2351
- 1863-2246
- ISBN
- 978-3-319-78208-9
- 9783319782072
- DOI
- 10.1007/978-3-319-78208-9_14
- language
- English
- LU publication?
- yes
- id
- 4bc5a87b-f5e1-404b-9be3-0273c5574103
- date added to LUP
- 2018-04-16 14:05:47
- date last changed
- 2024-04-01 04:23:09
@inproceedings{4bc5a87b-f5e1-404b-9be3-0273c5574103,
abstract = {{<p>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.</p>}},
author = {{Khoshahval, Samira and Farnaghi, Mahdi and Taleai, Mohammad and Mansourian, Ali}},
booktitle = {{Geospatial Technologies for All : Selected Papers of the 21st AGILE Conference on Geographic Information Science}},
editor = {{Mansourian, Ali and Pilesjö, Petter and Harrie, Lars and van Lammeren, Ron}},
isbn = {{978-3-319-78208-9}},
issn = {{1863-2351}},
keywords = {{Association rule mining; Behavioral pattern; K-furthest neighborhood; Personalized recommender assistant; Point of interest (POI); Serendipity}},
language = {{eng}},
month = {{01}},
pages = {{271--289}},
publisher = {{Springer International Publishing}},
series = {{Lecture Notes in Geoinformation and Cartography}},
title = {{A Personalized location-based and serendipity-oriented point of interest recommender assistant based on behavioral patterns}},
url = {{http://dx.doi.org/10.1007/978-3-319-78208-9_14}},
doi = {{10.1007/978-3-319-78208-9_14}},
volume = {{part F3}},
year = {{2018}},
}