國立虎尾科技大學 |

Smart Travel Recommender System.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Smart Travel Recommender System./
Author:	Ahmad, Bilal.
Description:	1 online resource (57 pages)
Notes:	Source: Masters Abstracts International, Volume: 57-06.
Contained By:	Masters Abstracts International57-06(E).
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9780438023598

Smart Travel Recommender System.
Ahmad, Bilal.

Smart Travel Recommender System. - 1 online resource (57 pages)

Source: Masters Abstracts International, Volume: 57-06.

Thesis (M.S.)--University of Missouri - Kansas City, 2018.

Includes bibliographical references

Many people around the globe live in areas that have unhealthy levels of air pollution. Such air pollution raises the risks of health problems. Current travel guidance systems such as Google Maps give recommendations to users mainly based on short travel time. Driven by the idea of improving air quality, mobility and quality of life for city residents, we contribute smart travel recommender system, which uses a recommendation engine to suggest the optimum commute mode based on variables such as air quality, distance, weather, and users' preferences. To make it more realistic and adaptive, our algorithm gives more weight to newer commute observations and preferences while suppressing the impact of the older ones. The recommendations are influenced by different predictor variables such as 'Distance', 'Air Quality', 'Weather' etc. Each variable is analyzed separately using a parameter called 'Phi-Correlation' coefficient to determine its impact on the commute mode choice. We proposed a ranking algorithm to calculate a score based upon the weighted impact of predictor variables and choose the one with highest score. We simulated different user profiles to determine the recommendations accuracy. The results proved that the recommender system quickly learns about user's travel patterns and generates relevant suggestions.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438023598Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Smart Travel Recommender System.
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300 $a 1 online resource (57 pages)
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500 $a Source: Masters Abstracts International, Volume: 57-06.
500 $a Adviser: Mohammad Amin Kuhail.
502 $a Thesis (M.S.)--University of Missouri - Kansas City, 2018.
504 $a Includes bibliographical references
520 $a Many people around the globe live in areas that have unhealthy levels of air pollution. Such air pollution raises the risks of health problems. Current travel guidance systems such as Google Maps give recommendations to users mainly based on short travel time. Driven by the idea of improving air quality, mobility and quality of life for city residents, we contribute smart travel recommender system, which uses a recommendation engine to suggest the optimum commute mode based on variables such as air quality, distance, weather, and users' preferences. To make it more realistic and adaptive, our algorithm gives more weight to newer commute observations and preferences while suppressing the impact of the older ones. The recommendations are influenced by different predictor variables such as 'Distance', 'Air Quality', 'Weather' etc. Each variable is analyzed separately using a parameter called 'Phi-Correlation' coefficient to determine its impact on the commute mode choice. We proposed a ranking algorithm to calculate a score based upon the weighted impact of predictor variables and choose the one with highest score. We simulated different user profiles to determine the recommendations accuracy. The results proved that the recommender system quickly learns about user's travel patterns and generates relevant suggestions.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Missouri - Kansas City. $b Computer Science. $3 1182463
773 0 $t Masters Abstracts International $g 57-06(E).
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