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A Personalized loca...
A Personalized location-based and serendipity-oriented point of interest recommender assistant based on behavioral patterns
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- Khoshahval, Samira (författare)
- K. N. Toosi University of Technology
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- Farnaghi, Mahdi (författare)
- Lund University,Lunds universitet,Institutionen för naturgeografi och ekosystemvetenskap,Naturvetenskapliga fakulteten,Dept of Physical Geography and Ecosystem Science,Faculty of Science,K. N. Toosi University of Technology
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- Taleai, Mohammad (författare)
- K. N. Toosi University of Technology
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- Mansourian, Ali (författare)
- Lund University,Lunds universitet,Mellanösternstudier,Centrum för Mellanösternstudier (CMES),Samhällsvetenskapliga institutioner och centrumbildningar,Samhällsvetenskapliga fakulteten,Middle Eastern Studies,Centre for Advanced Middle Eastern Studies (CMES),Departments of Administrative, Economic and Social Sciences,Faculty of Social Sciences,K. N. Toosi University of Technology
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Mansourian, Ali (redaktör/utgivare)
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Pilesjö, Petter (redaktör/utgivare)
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Harrie, Lars (redaktör/utgivare)
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van Lammeren, Ron (redaktör/utgivare)
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K N. Toosi University of Technology Institutionen för naturgeografi och ekosystemvetenskap (creator_code:org_t)
- 2018-03-24
- 2018
- Engelska 19 s.
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Ingår i: 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. - Cham : Springer International Publishing. - 1863-2351 .- 1863-2246. - 9783319782072 - 9783319782089 ; part F3, s. 271-289
- Relaterad länk:
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http://dx.doi.org/10...
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Naturgeografi (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Physical Geography (hsv//eng)
Nyckelord
- Association rule mining
- Behavioral pattern
- K-furthest neighborhood
- Personalized recommender assistant
- Point of interest (POI)
- Serendipity
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)
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