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

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

Standard

A Personalized location-based and serendipity-oriented point of interest recommender assistant based on behavioral patterns. / Khoshahval, Samira; Farnaghi, Mahdi; Taleai, Mohammad; Mansourian, Ali.

Geospatial Technologies for All : Selected Papers of the 21st AGILE Conference on Geographic Information Science. ed. / Ali Mansourian ; Petter Pilesjö; Lars Harrie; Ron van Lammeren. Vol. part F3 Cham : Springer International Publishing, 2018. p. 271-289 (Lecture Notes in Geoinformation and Cartography; Vol. part F3).

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

Harvard

Khoshahval, S, Farnaghi, M, Taleai, M & Mansourian, A 2018, A Personalized location-based and serendipity-oriented point of interest recommender assistant based on behavioral patterns. in A Mansourian , P Pilesjö, L Harrie & R van Lammeren (eds), Geospatial Technologies for All : Selected Papers of the 21st AGILE Conference on Geographic Information Science. vol. part F3, Lecture Notes in Geoinformation and Cartography, vol. part F3, Springer International Publishing, Cham, pp. 271-289, 21st AGILE Conference on Geographic Information Science, 2018, Lund, Sweden, 2018/06/12. https://doi.org/10.1007/978-3-319-78208-9_14

APA

Khoshahval, S., Farnaghi, M., Taleai, M., & Mansourian, A. (2018). A Personalized location-based and serendipity-oriented point of interest recommender assistant based on behavioral patterns. In A. Mansourian , P. Pilesjö, L. Harrie, & R. van Lammeren (Eds.), Geospatial Technologies for All : Selected Papers of the 21st AGILE Conference on Geographic Information Science (Vol. part F3, pp. 271-289). (Lecture Notes in Geoinformation and Cartography; Vol. part F3). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-78208-9_14

CBE

Khoshahval S, Farnaghi M, Taleai M, Mansourian A. 2018. A Personalized location-based and serendipity-oriented point of interest recommender assistant based on behavioral patterns. Mansourian A, Pilesjö P, Harrie L, van Lammeren R, editors. In Geospatial Technologies for All : Selected Papers of the 21st AGILE Conference on Geographic Information Science. Cham: Springer International Publishing. pp. 271-289. (Lecture Notes in Geoinformation and Cartography). https://doi.org/10.1007/978-3-319-78208-9_14

MLA

Khoshahval, Samira et al. "A Personalized location-based and serendipity-oriented point of interest recommender assistant based on behavioral patterns"., Mansourian , Ali and Pilesjö, Petter Harrie, Lars van Lammeren, Ron (editors). Geospatial Technologies for All : Selected Papers of the 21st AGILE Conference on Geographic Information Science. Lecture Notes in Geoinformation and Cartography. Cham: Springer International Publishing. 2018, 271-289. https://doi.org/10.1007/978-3-319-78208-9_14

Vancouver

Khoshahval S, Farnaghi M, Taleai M, Mansourian A. A Personalized location-based and serendipity-oriented point of interest recommender assistant based on behavioral patterns. In Mansourian A, Pilesjö P, Harrie L, van Lammeren R, editors, Geospatial Technologies for All : Selected Papers of the 21st AGILE Conference on Geographic Information Science. Vol. part F3. Cham: Springer International Publishing. 2018. p. 271-289. (Lecture Notes in Geoinformation and Cartography). https://doi.org/10.1007/978-3-319-78208-9_14

Author

Khoshahval, Samira ; Farnaghi, Mahdi ; Taleai, Mohammad ; Mansourian, Ali. / A Personalized location-based and serendipity-oriented point of interest recommender assistant based on behavioral patterns. Geospatial Technologies for All : Selected Papers of the 21st AGILE Conference on Geographic Information Science. editor / Ali Mansourian ; Petter Pilesjö ; Lars Harrie ; Ron van Lammeren. Vol. part F3 Cham : Springer International Publishing, 2018. pp. 271-289 (Lecture Notes in Geoinformation and Cartography).

RIS

TY - GEN

T1 - A Personalized location-based and serendipity-oriented point of interest recommender assistant based on behavioral patterns

AU - Khoshahval, Samira

AU - Farnaghi, Mahdi

AU - Taleai, Mohammad

AU - Mansourian, Ali

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

KW - Association rule mining

KW - Behavioral pattern

KW - K-furthest neighborhood

KW - Personalized recommender assistant

KW - Point of interest (POI)

KW - Serendipity

UR - http://www.scopus.com/inward/record.url?scp=85044825370&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-78208-9_14

DO - 10.1007/978-3-319-78208-9_14

M3 - Paper in conference proceeding

SN - 9783319782072

VL - part F3

T3 - Lecture Notes in Geoinformation and Cartography

SP - 271

EP - 289

BT - Geospatial Technologies for All

A2 - Mansourian , Ali

A2 - Pilesjö, Petter

A2 - Harrie, Lars

A2 - van Lammeren, Ron

PB - Springer International Publishing

CY - Cham

ER -