TY - JOUR
T1 - Personalized Tour Recommendation via Analyzing User Tastes for Travel Distance, Diversity and Popularity
AU - Lee, Jongsoo
AU - Shin, Jung Ah
AU - Chae, Dong Kyu
AU - Lee, Sang Chul
N1 - Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/4/1
Y1 - 2022/4/1
N2 - The goal of a tour recommendation is to recommend the best destinations according to the preferences of each tourist. The task of tour recommendation is challenging in that it not only has to consider the ratings, as do existing traditional recommendation problems, but it must also consider the personalization of the unique characteristics, such as diversity, travel distance, and popularity of the travel destination, which previous studies have failed to take into account. In this paper, we propose, for the first time, aspect personalization: we find out how important each user considers the diversity, distance and popularity of a travel destination when choosing where to visit. Then, we provide recommendations on tourist attractions by combining the personalized score for each factor and the predicted score. For the evaluation, we gathered user ratings and metadata of POIs from TripAdvisor and Naver. Experimental results showed that the proposed method had an 82%, 24% and 20% improvement in precision and a 129%, 35% and 22% improvement in recall in terms of top-1, top-2 and top-3 recommendations.
AB - The goal of a tour recommendation is to recommend the best destinations according to the preferences of each tourist. The task of tour recommendation is challenging in that it not only has to consider the ratings, as do existing traditional recommendation problems, but it must also consider the personalization of the unique characteristics, such as diversity, travel distance, and popularity of the travel destination, which previous studies have failed to take into account. In this paper, we propose, for the first time, aspect personalization: we find out how important each user considers the diversity, distance and popularity of a travel destination when choosing where to visit. Then, we provide recommendations on tourist attractions by combining the personalized score for each factor and the predicted score. For the evaluation, we gathered user ratings and metadata of POIs from TripAdvisor and Naver. Experimental results showed that the proposed method had an 82%, 24% and 20% improvement in precision and a 129%, 35% and 22% improvement in recall in terms of top-1, top-2 and top-3 recommendations.
KW - diversification
KW - personalization
KW - taste variations
KW - tour recommendation
KW - user taste prediction
UR - http://www.scopus.com/inward/record.url?scp=85127392276&partnerID=8YFLogxK
U2 - 10.3390/electronics11071120
DO - 10.3390/electronics11071120
M3 - Article
AN - SCOPUS:85127392276
SN - 2079-9292
VL - 11
JO - Electronics (Switzerland)
JF - Electronics (Switzerland)
IS - 7
M1 - 1120
ER -