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

Khoshahval, Samira; Farnaghi, Mahdi LU ; Taleai, Mohammad and Mansourian, Ali LU (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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Please use this url to cite or link to this publication:
author
organization
publishing date
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
in
Lecture Notes in Geoinformation and Cartography
volume
part F3
pages
19 pages
publisher
Springer Berlin Heidelberg
conference name
21st AGILE Conference on Geographic Information Science, 2018
external identifiers
  • scopus:85044825370
ISSN
1863-2351
ISBN
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
2018-04-16 14:05:47
@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    = {Lecture Notes in Geoinformation and Cartography},
  isbn         = {9783319782072},
  issn         = {1863-2351},
  keyword      = {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 Berlin Heidelberg},
  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},
  volume       = {part F3},
  year         = {2018},
}