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All Journal International Journal for Applied Information Management
Hsin-Wen Wei
Tamkang University, Taiwan
Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences

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Intelligent POIs Recommender System Based on Time Series Analysis with Seasonal Adjustment Meng-Kuan Chen; Hsin-Wen Wei; Wei-Tsong Lee
International Journal for Applied Information Management Vol. 2 No. 2 (2022): Regular Issue: July 2022
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v2i2.28

Abstract

Recommender systems have been applied on a variety of applications including movies, music, news, books, research articles, search queries, and travel information. Instead of searching travel information from the extremely huge amount of travel data, a personalized travel recommender system is desired. However, an inappropriate travel recommendation may result from a wrong season, even if it is already a correct location. The current recommender systems from time to time make an inappropriate commendation without considering the seasonal factor. In order to resolve the discrepancy, the seasonal factor should have been taken into consideration when making a good travel recommender system. Therefore, this study has taken the trend analysis, time series, and seasonal factor into considerations to cope with the above mentioned discrepancy and to make the travel recommender system renders a better fit.
Intelligent POIs Recommender System Based on Time Series Analysis with Seasonal Adjustment Meng-Kuan Chen; Hsin-Wen Wei; Wei-Tsong Lee
International Journal for Applied Information Management Vol. 2 No. 2 (2022): Regular Issue: July 2022
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v2i2.28

Abstract

Recommender systems have been applied on a variety of applications including movies, music, news, books, research articles, search queries, and travel information. Instead of searching travel information from the extremely huge amount of travel data, a personalized travel recommender system is desired. However, an inappropriate travel recommendation may result from a wrong season, even if it is already a correct location. The current recommender systems from time to time make an inappropriate commendation without considering the seasonal factor. In order to resolve the discrepancy, the seasonal factor should have been taken into consideration when making a good travel recommender system. Therefore, this study has taken the trend analysis, time series, and seasonal factor into considerations to cope with the above mentioned discrepancy and to make the travel recommender system renders a better fit.
Co-Authors Meng-Kuan Chen Meng-Kuan Chen Wei-Tsong Lee